Thi Anh Thu Dang Thesis
IMPACT OF AMBIENT TEMPERATURE ON HOSPITAL ADMISSIONS FOR ACUTE MYOCARDIAL INFARCTION IN THE CENTRAL COAST OF VIETNAM
Dang Thi Anh Thu MD, MPH
Submitted in fulfilment of the requirements for the degree of Doctoral Philosophy
Queensland University of Technology School of Public Health and Social Work Faculty of Health
2018
Keywords
Acute myocardial infarction, ambient temperature, cold spells, distributed lag non- linear model, generalised linear model, heat waves, cold spells, meteorological factors, morbidity, pre-hospital delay time, seasonality.
Impact of ambient temperature on hospital admissions for acute myocardial infarction in the central coast of Vietnam i
Abstract
Background: Acute myocardial infarction (AMI) is a major health problem worldwide and in Vietnam. In addition to the list of recognised genetic, behavioural and environmental risk factors for AMI, there is increasing concern about the ambient temperature effects associated with global climate change on cardiovascular diseases in general, and AMI in particular. Vietnam is one of the countries that has been forecast to suffer more disadvantageous weather and natural disasters due to human-induced global warming. However, very few studies on temperature-related health effects have been conducted in Vietnam. In particular, to date, no study has focused on possible links between air temperature extremes and AMI, one of the most important causes of morbidity and mortality in the country. The present study in the central coast region of Vietnam investigated ambient temperature effects and hospital admissions for AMI. This study aimed to: 1) explore the pre-hospital delay period and its associated factors among AMI patients living in the Central Coast region of Vietnam from 2008 to 2015, 2) examine the long-term trends and seasonality of AMI hospital admissions, 3) estimate the short-term effects of ambient temperature on daily adult AMI hospital admissions , and 4) evaluate the added effects of extreme temperature conditions (heat waves and cold spells) on daily adult AMI hospital admissions during this period.
Methods: A retrospective ecological study design was used. Data were collected from a total of 3,328 hard-copy medical records of AMI patients hospitalised in three highest-level hospitals in the Central Coast region of Vietnam from 2008 to 2015. Information on weather and influenza circulation was obtained from the Vietnamese National Hydrometeorology and Environment Network Centre (National Hydro- meteorological and Environment Network Center, 2016) and Statistical Yearbooks of Infectious Diseases (Department of Preventive Medicine-Vietnamese Ministry of Health 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016). A time-series analytic approach with a generalised linear model /distributed lag non-linear model, and negative binomial regression were used to examine the pre-hospital delay time period, seasonality of AMI hospital admissions, and impacts of ambient temperature variations/extremes on local AMI hospital admissions. Long-term trends, the seasonality of AMI hospital admissions, humidity, wind speed, air pressure, influenza-
ii Impact of ambient temperature on hospital admissions for acute myocardial infarction in the central coast of Vietnam
like illness counts, and weekend days and holidays were also controlled for when estimating the effects of temperature variations/extremes.
Results: The results showed that 46.1% of local AMI patients experienced delay in their first presentation to a medical centre by 12 or more hours after the disease occurrence. This put the patients at higher risk of not receiving reperfusion therapy, an effective AMI treatment. The groups likely to have longer pre-hospital delay time were women, the elderly, patients hospitalised at low-level medical centres, those who had less severe health conditions at the onset, those who had non-ST elevated myocardial infarction, and individuals with at least one comorbidity.
AMI hospital admission rates were significantly higher in winter compared to those in summer. January-February was the peak time for AMI hospitalisations, while the hospitalisation rates reached their trough in July-August. Interestingly, there were significantly more daily hospital admissions due to AMI when the ambient temperature was high in the South Central Coast region (savanna tropical climate); while in the North Central Coast region (monsoon tropical climate), a significantly higher rate of AMI hospitalisation was found for lower temperatures. Moreover, on days of heat wave exposure, the rate of daily hospital admissions for AMI was 22% (95% CI: 4%–44%) higher than those not exposed heat waves on the South Central Coast. However, on days of cold spell exposure, the rate of daily hospital admissions for AMI was 35% (95% CI: 3%–76%) higher than those not exposed cold spells on the North Central Coast. Males, younger age groups, those with ST-segment elevation myocardial infarction, and patients without comorbidities showed significant increases in risk of AMI admissions associated with heat waves in the South Central Coast region. In contrast, being male and elderly were found to be significant risk factors for AMI admissions in relation to cold spells in the North Central Coast region.
Conclusion and Discussion: The effect of seasonality and ambient temperature extremes on hospitalisation due to AMI differed between the North Central Coast and South-Central Coast regions, as well as among different population subgroups. Among studies that explore the associations between ambient temperature and AMI morbidity worldwide, there are inconsistent findings cross regions, countries, latitudes and climates (Bijelović et al., 2017; Fernández-García et al.; Honda et al., 2016; Kwon et al., 2015; Ravljen et al., 2018; Tian et al., 2016; Loughnan et al., 2014; Mohammadi et al., 2018; Morabito et al., 2006; Wijnbergen et al., 2012; Yamaji et al., 2017). The
Impact of ambient temperature on hospital admissions for acute myocardial infarction in the central coast of Vietnam iii
current study adds to the global evidence regarding sub-regional variation. While the effects were not large, they were significant and importantly, the study shows they are detectable even across sub-regions of a primarily tropical-climate country.
The study could contribute to the development of targeted public health strategies to reduce pre-hospital delay such as introducing education programs about early signs, symptoms and the importance of early hospitalisation after initial signs of the onset of AMI. It is necessary to raise the awareness of the population, especially vulnerable groups (such as the elderly, outside workers, and poorer people) about the risk of AMI from exposure to temperature extremes. We recommend the Vietnamese Government should incorporate health messages relating to AMI (and other serious environment-related health conditions) in the form of warnings during the weather forecasts in terms of extreme weather, as happens in many countries worldwide Further, the government should incorporate recognition of the population health impacts of extremes of temperature into legislation and national targets CVDs prevention and climate change adaptation.
iv Impact of ambient temperature on hospital admissions for acute myocardial infarction in the central coast of Vietnam
Table of Contents
Keywords ...... i Abstract ...... ii Table of Contents ...... v List of Figures ...... viii List of Tables ...... x List of Abbreviations ...... xi Outcomes Arising from this Thesis ...... xiii Statement of Original Authorship ...... xiv Statement of Contribution ...... xiv Acknowledgements ...... xv Chapter 1: Introduction...... 1 1.1 Background ...... 1 1.1.1 Studied site – Central Coast region of Vietnam ...... 3 1.2 Context ...... 5 1.3 Aims and Research Significance ...... 6 1.3.1 Aims and objectives ...... 6 1.3.2 The significance of the research ...... 7 1.4 Thesis Outline ...... 7 Chapter 2: Literature Review ...... 9 2.1 Overview of Acute Myocardial Infarction...... 9 2.1.1 Acute myocardial infarction ...... 9 2.1.2 Global and national burdens of CVDs/IHDs/AMI ...... 12 2.1.3 Risks and associated factors of acute myocardial infarction ...... 15 2.1.4 Seasonality of acute myocardial infarction ...... 17 2.1.5 Early treatment of acute myocardial infarction and the importance of in-time hospital arrival ...... 18 2.2 Environmental Related Factors ...... 20 2.2.1 Ambient temperature ...... 20 2.2.2 Other meteorological factors ...... 21 2.2.3 Air pollution ...... 23 2.2.4 Influenza ...... 24 2.3 Review of Studies on Ambient Temperature and Acute Myocardial Infarction ...... 25 2.3.1 Temperature variation/extremes effects ...... 25 2.3.2 Mechanism for temperature effect on the morbidity of acute myocardial infarction ...... 40 2.4 Related Studies in the Vietnamese Context ...... 42 2.5 Knowledge Gaps ...... 43 2.6 Research Problems...... 44
Impact of ambient temperature on hospital admissions for acute myocardial infarction in the central coast of Vietnam v
Chapter 3: Methodology...... 46 3.1 Study Design and Research Conceptual Framework ...... 46 3.1.1 Study design ...... 46 3.1.2 Research conceptual framework ...... 46 3.2 Study Areas and Populations ...... 48 3.2.1 Khanh Hoa ...... 50 3.2.2 Thua Thien Hue ...... 50 3.2.3 Quang Binh ...... 51 3.3 Ethical Approval ...... 55 3.4 Data Collection Process ...... 55 3.4.1 Stage I: Pre-pilot negotiations and consultation with key stakeholders ...... 58 3.4.2 Stage II: Pilot Study ...... 60 3.4.3 Stage III: Main data collection ...... 61 3.4.4 Stage IV: Expert consultation on long - term trends of hospital admissions due to acute myocardial infarction and influenza data collection ...... 64 3.4.5 Role of the PhD researcher and research assistants for data collection process ...... 65 3.4.6 Reliability and rigour of the data and their management ...... 65 3.5 Acute Myocardial Infarction Selection Criteria and Collection Information ...... 66 3.5.1 Acute myocardial infarction selection data ...... 66 3.5.2 Collection information ...... 68 3.6 Data Analysis ...... 70 3.6.1 Descriptive analysis of acute myocardial infarction cases ...... 70 3.6.2 Pre-hospital delay time for acute myocardial infarction ...... 70 3.6.3 Long-term trends and seasonal analysis ...... 71 3.6.4 Short–term effects of ambient temperature on hospital admissions due to acute myocardial infarction ...... 72 3.6.5 Added effects of extreme temperatures on hospital admissions due to acute myocardial infarction ...... 75 3.6.6 Sensitivity analysis ...... 79 Chapter 4: Descriptive Statistics of Acute Myocardial Infarction Patients and the Factors Associated with Delays Involved in First Medical Centre Admissions ...... 81 4.1 Introduction ...... 81 4.2 Demographic Characteristics and Disease History of Acute Myocardial Infarction Patients ...... 83 4.3 Factors Associated with the Delay Time of First Medical Centre Admissions of Acute Myocardial Infarction Patients ...... 87 4.4 Discussion ...... 92 4.5 Conclusion ...... 97 Chapter 5: Analysis of Seasonality in Hospital Presentations for Acute Myocardial Infarction in Central Vietnam ...... 99 5.1 Introduction ...... 99 5.2 Trends of Hospital Admissions in the Three Provinces ...... 100 5.3 Seasonality of Hospital Admissions due to Acute Myocardial Infarction ...... 101
vi Impact of ambient temperature on hospital admissions for acute myocardial infarction in the central coast of Vietnam
5.4 Seasonality in Hospital Admissions due to Acute Myocardial Infarction in different Gender and Age groups ...... 104 5.5 Discussion ...... 105 Chapter 6: Short-term Effects of Temperature on Hospital Admissions of Acute Myocardial Infarction in Central Vietnam ...... 112 6.1 Introduction ...... 112 6.2 Descriptive Time-series Characteristics of Hospital Admissions due to Acute Myocardial Infarction ...... 114 6.3 Heat effect in the South Central Coast region ...... 117 6.4 Cold effect in the North Central Coast region ...... 121 6.5 Discussion ...... 125 6.6 Conclusion ...... 128 Chapter 7: Added Heat Wave and Cold Spell Effects on Hospital Admissions of Acute Myocardial Infarction in Central Vietnam ...... 129 7.1 Introduction ...... 129 7.2 Heat Wave Effect in the South Central Coast of Vietnam ...... 131 7.3 Cold Spell Effect in the North Central Coast of Vietnam ...... 134 7.4 Discussion ...... 137 7.5 Conclusion ...... 142 Chapter 8: General Discussion and Recommendations ...... 143 8.1 Key Findings ...... 143 8.1.1 Long-term trends and seasonality of hospital admissions due to acute myocardial infarction ...... 143 8.1.2 Short-term effects of ambient temperature on hospital admissions due to acute myocardial infarction ...... 145 8.1.3 Added effects of temperature extremes on the hospital admissions due to acute myocardial infarction ...... 146 8.1.4 Pre-hospital delay time of patients with acute myocardial infarction ...... 148 8.2 Strengths and Limitations ...... 150 8.2.1 Strengths ...... 150 8.2.2 Limitations...... 151 8.2.3 Sources of bias and confounding ...... 153 8.3 Contributions of this Thesis ...... 154 8.4 Implications for Further Research ...... 156 8.5 Recommendations...... 157 References ...... 160 Appendices ...... 185
Impact of ambient temperature on hospital admissions for acute myocardial infarction in the central coast of Vietnam vii
List of Figures
Figure 2.1 Pathologic and Clinical ST-Segment Elevation Acute Myocardial Infarction (STEMI) and Non-STEMI Acute Coronary Syndromes ...... 11 Figure 2.2 The probability of dying between ages 30 and 70 years from the four main Noncommunicable Diseases in Vietnam ...... 14
Figure 2.3 Plausible biological mechanisms linking cold exposure to atherothrombotic events ...... 40 Figure 3.1 Risk factors for AMI ...... 47 Figure 3.2 Main conceptual framework for this study ...... 47 Figure 3.3 Time frame from temperature exposure to presentation at admitted hospitals of AMI patients ...... 48 Figure 3.4 Map of the Central Coast of Vietnam and location of the three study provinces ...... 49 Figure 3.5 Data collection procedure ...... 57 Figure 3.6 Flowchart of data collection process in studied hospitals ...... 63 Figure 3.7 Criteria for AMI diagnosis ...... 67
Figure 3.8 Control selection strategies ...... 80 Figure 4.1 Distribution of AMI patients by Age ...... 84 Figure 4.2 Association between Age and delay referral to first medical centre of AMI patients ...... 88 Figure 5.1 Rates for hospital admissions (Jan 2008-Dec 2015) of AMI patients by provinces ...... 101 Figure 5.2 Numbers of AMI HAs by seasons and provinces ...... 102 Figure 5.3 Numbers of AMI HAs by seasons and year ...... 103 Figure 5.4 Monthly distributions of temperature ranges in the studied provinces .. 109 Figure 6.1: Seasonal distribution of monthly AMI HAs and temperature in the South Central Coast region, 2008–2015 ...... 116 Figure 6.2 Seasonal distribution of monthly AMI HAs and temperature in the North Central Coast region, 2008–2015 ...... 116 Figure 6.3 Three-dimensional graph showing the relative risk along temperature range and lags, with reference temperature at 23.4 °C for the South Central Coast (Khanh Hoa) region ...... 118 Figure 6.4 The overall effect (lag 0-21) of temperature on hospital admissions due to AMI in the South Central Coast region, Vietnam in 2008-2015 (red line), with 95% confidence intervals (grey area) ...... 119
viii Impact of ambient temperature on hospital admissions for acute myocardial infarction in the central coast of Vietnam
Figure 6.5 The overall effects (lag 0-21) of temperature for all subgroups on hospital admissions due to AMI in the South Central Coast region, Vietnam in 2008-2015 (red line), with 95% confidence intervals (grey area) ...... 120 Figure 6.6 Relative risk (RR) of AMI admissions by temperature and lag using cross-basis smoothing ...... 121 Figure 6.7 Three-dimensional graph of the relative risk along temperature range and lags, with a reference temperature at 21.2 °C for the North Central Coast (Thua Thien Hue and Quang Binh) region ...... 122 Figure 6.8 Overall effects (lag 0-21) of temperature on hospital admissions due to AMI in the North Central Coast region in 2008-2015 (red lines), with 95% confidence intervals (grey areas) ...... 123 Figure 6.9 The overall effects (lag 0-21) of temperature for all subgroups on hospital admissions due to AMI in the North Central Coast region, Vietnam in 2008-2015 (red line), with 95% confidence intervals (grey area) ...... 123 Figure 6.10 Relative risk (RR) of AMI admissions by temperature and lag using cross-basis smoothing ...... 124 Figure 7.1 The relative risk at exposed day of AMI patients by different heat wave definitions before hospitalisation during the period 2008–2015 in the South Central Coast region, Vietnam ...... 132 Figure 7.2 Estimates of the relative risk for the heat wave effect at the 98th temperature percentile with three consecutive days on AMI admissions at different lags ...... 133 Figure 7.3 Relative risk of daily AMI admissions associated with the added heat wave effect at the temperature of the 98th percentile with three consecutive days for different subgroups ...... 134 Figure 7.4 The cumulative relative risks for the first 14 days of AMI patients by different cold spell definitions before hospitalisation during the period 2008–2015 in the North Central Coast region, Vietnam ...... 135 Figure 7.5 Estimates of the relative risk for the cold spell effect at the 2nd temperature percentile with three consecutive days on AMI admissions at different lags ...... 136 Figure 7.6 Relative risk of daily AMI admissions associated with the added cold spell effect at the temperature of the 2nd percentile with three consecutive days for different subgroups ...... 137
Impact of ambient temperature on hospital admissions for acute myocardial infarction in the central coast of Vietnam ix
List of Tables
Table 2.1 Characteristics of studies on ambient temperature and AMI HAs ...... 30 Table 3.1 Demographic and climatic characteristics of the three studied provinces ...... 52 Table 3.2 Descriptive statistics of socio-economic factors in three studied provinces in 2015 ...... 53 Table 3.3 Characteristics of daily meteorological factors by provinces ...... 54 Table 3.4 Summary of data set and data sources used in this study ...... 69 Table 3.5 Summary of heat wave events during the period 2008-2015 using different heat wave definitions (HWDs) in South Central Coast region, Vietnam...... 76 Table 3.6 Summary of cold spell events during the period 2008–2015 using different cold spell definitions (CSDs) in the North Central Coast region, Vietnam...... 77 Table 4.1 Demographic characteristics of AMI patients by provinces ...... 83 Table 4.2 Hospital access and AMI conditions of patients by provinces ...... 86 Table 4.3 Treatment and health status at discharge of AMI patients by provinces ...... 87 Table 4.4. Distribution of AMI patients with respect to delay referral to first medical centre by provinces ...... 88 Table 4.5 Distribution of AMI patients with respect to related factors and delay referral to first medical centre ...... 89 Table 4.6 Odd ratios of delay time for first medical centre admissions in AMI patients with respect to significant related factors ...... 91 Table 5.1 Summary of seasonal comparison of AMI HAs by provinces ...... 103 Table 6.1 Descriptive statistics of monthly AMI hospital admissions by age, gender, and health status in the South Central Coast region and the North Central Coast region, 2008-2015 ...... 115 Table 6.2 Distribution of selected meteorological and influenza-like illness variables in the South Central Coast region and the North Central Coast region, 2008-2015 ...... 115 Table 6.3 Spearman coefficient of AMI, meteorological factor and influenza- like illness in the South Central Coast region and the North Central Coast region, 2008–2015 ...... 117
x Impact of ambient temperature on hospital admissions for acute myocardial infarction in the central coast of Vietnam
List of Abbreviations
ACS: Acute coronary syndrome
AMI: Acute myocardial infarction
CA2: 2-day cumulative average
CABG: Coronary artery bypass grafting
CHD: Coronary heart diseases
CIs: Confidence intervals
CVDs: Cardiovascular diseases
DALYs: Disability adjusted life year
DLNM: Distributed lag non-linear model
ECG: Electrocardiograph
GLMs: Generalised linear Models
HAs: Hospital admissions
HI : Heat Index
HRECs: Human Research Ethics Committees
HUMP: Hue University of Medicine and Pharmacy
ICD–10: International Classification of Disease version 10
IHD: Ischemic heart disease
IQR: Interquartile range
IRR: Incident rate ratio
MI : Myocardial infarction
Non-STEMI: Non-ST segment elevation myocardial infarction
PAF: Population attributable fraction
PCI: Percutaneous coronary intervention
PET: Physiologically equivalent temperature
Impact of ambient temperature on hospital admissions for acute myocardial infarction in the central coast of Vietnam xi
RRs: Relative Risks
SES: Socio-economic status
STEMI: ST-segment elevation myocardial infarction
UHREC: University Human Research Ethics Committee
WHO: World Health Organisation
YLLs: Years of life lost
xii Impact of ambient temperature on hospital admissions for acute myocardial infarction in the central coast of Vietnam
Outcomes Arising from this Thesis
Abstracts–oral presentations
Dang, T. A. T, Wraith, D., Dunne, M. Bambrick, H., Tong, S., Nguyen, D., & Thai, T. T. (2017). Ambient Temperature and Acute Myocardial Infarction in Vietnam. IHBI Inspires Annual Conference 2017 (Queensland University of Technology, Brisbane, Australia).
Dang, T. A. T, Wraith, D., Dunne, M. Bambrick, H., Tong, S., & Nguyen, D., (2017). Seasonality of hospital admissions for acute myocardial infarction in Central Coast of Vietnam. The 29th Annual Scientific Conference of the International Society of Environmental Epidemiology (Sydney, Australia).
Impact of ambient temperature on hospital admissions for acute myocardial infarction in the central coast of Vietnam xiii QUT Verified Signature
QUT Verified Signature
Acknowledgements
I would like to thank the following people who have supported and helped me during my PhD candidature.
First, I would like to say thank you to the Australia Award Scholarship, previously known as Australia Development Scholarship, who offered me a scholarship to study at the Queensland University of Technology in Australia, so that I could achieve greater knowledge about health sciences. This is one of the best new universities in Australia and has a wonderful academic environment, from which I gained lots of new experiences. My special thanks also to Australia Award Scholarship officers Ms Dam Thi Phuong Thao, Ms Zia Song, Ms Youngnam Baek, Ms. Michelle Fernando, and Mr Lio Lay, who were very friendly and always willing to help me with the administrative aspects during my study.
I am deeply grateful to my supervisory team: Dr Darren Wraith, Professor Michael P. Dunne, Professor Shilu Tong, A/Professor Nguyen Dung and Professor Hilary Bambrick, who supported me during this research. Thank you for your great understanding, inspiration, and intellectual guidance. Especially, I would like to express my sincere appreciation to Professor Dunne, who not only criticised my study in broad aspects and gave extensive feedback on drafts, but also gave me enormous encouragements to overcome difficulties during the study and adapting to life overseas. My current brightened career would not have been achieved if I had not met him and got his great support for the last decade.
My sincere thanks go to my colleagues and friends who participated in this research as researcher assistants, as well as the individuals and institutions that made this research possible:
A/Professor Vo Van Thang, A/Professor Nguyen Hoang Lan, A/Professor Hoang Trong Sy, Dr Nguyen Huu Nghi, Dr Nguyen Dinh Minh Man, Dr Ngo Thi Dieu Huong, Dr Nguyen Thanh Gia, Ms Phung Thi Thu Thuy, Ms Nguyen Thi Bich Tam, Ms Do Thi Ngoc, Dr Vo Nu Hong Duc from the Public Health Faculty of Hue University of Medicine and Pharmacy;
Impact of ambient temperature on hospital admissions for acute myocardial infarction in the central coast of Vietnam xv
A/Professor Nguyen Minh Tam, Dr/ Luong Thanh Bao Yen, Ms Jill Nalder, Ms Nguyen Thi Thu Thuy from Hue Institute for Community Health Research; Dr Pham Huu Tri, Dr Mai Xuan Anh, Dr Nguyen Thi Thanh Thanh, Ms Nguyen Thi Tan Anh, Mr Nguyen Cuu Ngoc Son, Ms Phan Le Xuan Dieu from Hue Central Hospital; Dr Duong Phan Bich Hai, Dr Hoang Van Duc from the Health Department of Thua Thien Hue province; Dr Huynh Van Thuong, Dr Nguyen Dac Thuan; Ms Tran Thi Dieu Khanh, Dr Nguyen Nguyen Nguyet, Dr Nguyen Dang Dinh Thi from Khanh Hoa General Hospital; Dr Le Tan Phung from the Health Department of Khanh Hoa province; Dr Tran Linh Giang, Ms Nguyen Thi Lieu, Dr Hoang Minh Thanh Tu, Dr Doan Quoc Huy from the Vietnam-Cuba Friendships Hospital; Dr Duong Thi Phuc from the Health Department of Quang Binh province; Ms Phan Thanh Nga from Quang Binh Medical College; Dr Nguyen Van Hung from Dak Lak General Hospital; Dr Nguyen Dao Thien An, Dr Nguyen Que Chau, Dr Vo Thi Linh Dan, Dr Ho Thi Hao, Dr Pham Ngoc Hung, Dr Nguyen Thi Minh Trang, Dr Nguyen Dinh Tung, Dr Nguyen Thi Thuy A, Dr Le Hoang Thieu, Dr Nguyen Thi Thuy B, Dr Dao Ngoc Anh, Dr Nguyen Ngoc Giau, Mr Huynh Ngoc Toan, Ms Le Thi Mai Hoa, Ms Phan Thi Ngoc Binh, Ms Vo Thi Thuong: Dr Thai Thanh Truc, Dr Tran Ngoc Dang from the University of Medicine and Pharmacy at Ho Chi Minh City.
As a student from a non-English-speaking background, I also want to thank Dr Martin Reese and Dr Emma Caukill for their help with the English language and presentation. I also thank Dr Sue Naish for her great comments on my manuscripts and thesis. Many thanks to Dr Seiji Humphries, for his careful editing work on this thesis. I would also like to thank professional editor, Ms Kylie Morris, who provided copyediting and proofreading services, according to university-endorsed guidelines and the Australian Standards for editing research theses. xvi Impact of ambient temperature on hospital admissions for acute myocardial infarction in the central coast of Vietnam
Many thanks to all of the staff members in the Faculty of Health, School of Public Health and Social Work and the QUT Office of Research who supported me and provided an excellent environment for my study.
I want to particularly thank Professor Wenbiao Hu, Dr Zhiwei Xu, Mr Jian Cheng, Mr Mohammad Zahid Hossain, Mr Yuzhou Zhang, Ms Amanda Murphy, Ms Rokeya Akter, Ms Maryam Ghazani, Ms Ning Wang, Ms Shafkat Jahan, Ms Kang Kang Liu and our environmental epidemiologist group at QUT, who gave me many useful tips on statistical analysis and software use, as well as on academic writing. Their warm-hearted support and friendship made my study life much easier in Brisbane.
I would like to display my special thanks to my parents, my parents-in-law, and my lovely family members for their love, encouragement, and emotional support to motivate me to finish the course and the thesis. Special thanks also to my family and friends in Durack, Moorooka, and Herston Village, Brisbane, whose support made my life more beautiful during my stay in Brisbane, Australia.
Finally, the most important people that I would like to express my special thanks to are my lovely, dear husband, Tran Quang Vinh, and my dear daughter, Tran Tu Anh, who were always by my side, who understood me and encouraged me to complete the study, who always shared the happiness and sorrows of my interesting but challenging PhD life. It was difficult for a man like Vinh to quit his wonderful job in Vietnam to support me in a foreign country like Australia. Thanks also to my little 7-year-old daughter whose presence encourages me to do things with the highest effort and to complete this research and always gave me big hugs at the end of the day.
Impact of ambient temperature on hospital admissions for acute myocardial infarction in the central coast of Vietnam xvii
Chapter 1: Introduction
Background This section provides an overview of the study. It includes the background of the study and an introduction of the Central Coast region of Vietnam. It described the context of the study, aims and research significance, and gives a schematic outline of the Thesis.
1.1 BACKGROUND
Acute myocardial infarction (AMI) is a major social and health issue worldwide and in Vietnam, a developing country that has experienced a significant transition in the disease pattern in the past decades (Nguyen et al., 2012; Vietnamese Ministry of Health, 2011; World Health Organization, 2018). This serious health condition involves one or a few total occlusions of coronary arteries, leading to a complete lack of oxygen and nutrients and causing cardiac muscle necrosis (Anderson & Morrow, 2017; Ha, 2010). Patients with AMI can die quickly or suffer from severe health complications if they do not receive appropriate treatments within a few hours of disease onset (Anderson & Morrow, 2017). It is one of the two most common clinical manifestations of ischemic heart diseases (IHDs), while IHDs account for the majority of cardiovascular diseases (CVDs) – these diseases have become a leading cause of death globally (Wichmann, Rosengren, Sjöberg, Barregard, & Sallsten, 2013).
The adverse impacts of ambient temperature on mortality and morbidity have been recognised as a significant public health issue, and these impacts are expected to worsen due to continuing changes in climate (Xu et al., 2014). Evidence has shown that adverse temperature variation and extremes are likely to increase CVDs mortality and morbidity. For instance, Franchini and Mannucci (2015) concluded that extreme heat events were associated with exacerbations of pre-existing respiratory and CVDs. Both extremely cold and extremely hot temperatures have been found to increase mortality from ischemic heart diseases in China (Guo et al., 2013); with not only absolute temperatures, but also temperature variations (e.g., diurnal temperature range) being demonstrated to pose risks for human health (Cheng et al., 2014; Xu et al., 2013). The nature of the association is complex; a number of studies have reported that the effects of temperature may vary in different subclasses of CVDs (Bijelović et al.,
Chapter 1: Introduction 1
2017). These differences may arise due to the different nature of each disease and different physiological processes induced by heat or cold stress (Urban, Davídkovová, & Kyselý, 2014). While mortality rates due to chronic CVDs (atherosclerosis, chronic ischemic heart diseases) are especially increased by heat stress, those related to acute CVDs (myocardial infarction), are most exacerbated by cold stress (Madrigano et al., 2013; Tian, Qiu, Sun, & Lin, 2016). The different responses of individual CVDs to heat and cold stress represent an important finding that means that the specific impact on AMI might not be consistent or predictable across countries and environmental contexts. Further research is needed to generate information that can be used by health authorities to issue better-targeted alerts to the public regarding heat and cold stress.
Evidence regarding the impact of temperature on the development of myocardial infarction (MI) is not as well developed or as comprehensive as that on temperature and all-cause mortality or CVDs in general (Casas, Santos, Chiocheti, & De Andrade, 2016; Moghadamnia et al., 2017; Phung, Guo, Thai et al., 2016; Yang, Li, Wang, Huang & Lu, 2015). Findings from time-series research have been inconsistent with regards to AMI, with the majority reporting effects for cold ( Bhaskaran et al., 2010; Ravljen, Hovelja & Vavpotič, 2018; Vasconcelos, Freire, Almendra, Silva & Santana, 2013; Verberkmoes et al., 2012) and a few for heat (Loughnan, Nicholls & Tapper, 2010a; Mohammadi, Soori, Alipour, Bitaraf & Khodakarim, 2018), while a number showed the impact of both ( Loughnan, Nicholls, & Tapper, 2010b; Madrigano et al., 2013; Yamaji et al., 2017). These inconsistencies may be due to a number of factors, including different methodologies, population characteristics, and climate zone variations (Madrigano et al., 2013). In addition, few studies have been undertaken in areas with sub-tropical or tropical climates (Bijelović et al., 2017; Goggins, Chan & Yang, 2013).
It is important to identify whether there are subgroups that are vulnerable to the impact of temperature on AMI occurrence, as preventative approaches can be targeted more effectively towards those most at risk from ambient temperature extremes. The elderly, females, and those with previous coronary heart disease have previously been identified as having higher risks for AMI hospital admissions (HAs) in relation to temperature (Bhaskaran et al., 2010; J. H. Lee et al., 2010; Misailidou, Pitsavos, Panagiotakos, Chrysohoou, & Stefanadis, 2006). It is important to emphasise that evidence regarding the impacts of temperature extremes on these vulnerable groups
2 Chapter 1: Introduction
also varies. For example while m any studies have found that the elderly are likely to be at higher risk for AMI morbidity in relation to low temperature exposure (Bhaskaran et al., 2010; Honda, Fujimoto, & Miyao, 2016; Misailidou et al., 2006), some do not. J. Lee at al. (2010) found a significant negative correlation between daily HAs for AMI, patient age and mean air temperature, with a stronger association for those of younger ages.
Vietnam is one of the countries that has been forecast to experience more erratic adverse weather and natural disasters (Rubin, 2014). It has been reported that Vietnam’s average temperature rose by 0.14°C per decade between 1951–2000, particularly due to hotter summers in recent years, with average monthly temperatures increasing up to 0.3°C per decade (Rubin, 2014). In addition, the North Central region of the country annually suffers from many cold spells that affect wide areas and cause severe health impacts for people, livestock, and poultry (Vietnamese National Center for Hydro-Meteotological Forcasting [NCHMF]), 2014). However, very few studies on temperature-related health effects have been conducted in Vietnam. In particular, there has been very limited research about CVDs and AMI focused on the central region compared to the south and the north of the country, even though the central region suffers more from extreme weather and natural disasters than any other part of Vietnam (Vietnamese Ministry of Health, US Agency for International Development, National Institute of Hygiene and Epidemiology and Oxford University, 2014).
1.1.1 Studied site – Central Coast region of Vietnam The Central Coast region is comprised of 14 provinces (Nguyen Chau Giang, 2012; Vietnamese Ministry of Education and Training, 2018a; 2018b). It is the longest but narrowest region of Vietnam, stretching along 10 degrees of latitude (from 200N to 100N) from Thanh Hoa province to Binh Thuan Province, and between 1030E-1090E longitudinally. It neighbours the Lao PDR and Central Highlands of Vietnam to the West, South China Sea to the East, and the Northwest region and Red River Delta of Vietnam to the north. The region has an area of 95,895 km2 and occupies 29% of the country's land. Regarding the topography of the region, it features a gradual decrease in altitude from west to east, from mountainous areas to a hilly midland, down to the plains in the coastal strip of sand dunes and out to the coastal islands (Nguyen Chau Giang, 2012; Vietnamese Ministry of Education and Training, 2018a; 2018b).
Chapter 1: Introduction 3
In 2011, the total population of the region was 19,046,500, accounting for 21.7% of the national population, with 9,425,500 (49.5%) men. The average population density is 199 people/km2, while the national average is 265 people/km2. The proportion of urban population is 26.2%, and the rural population is 73.8% (General Statistics Office of Vietnam, 2018). The dominant Viet or Kinh ethnic groups constitutes up to 90% of the population and are concentrated mainly in the coastal plains of the region. Hilly areas and mountainous areas of the west are homelands for ethnic minorities such as Thai, Muong, Mong, Tho, Kho mu, Bru-Van Kieu, Chut, O Du, Catu, Cham etc. The economics of the region are still mainly based on agriculture, with forestry and fishery accounting for nearly 50% of the workforce in this area. The Central Coast region is in the monsoon tropical area, under the influence of the weather in the north and in the south of Vietnam. Based on differences in latitude and the marked variety in topographical relief, the climate of this region is divided into two main areas, the North Central and the South Central Coast regions (Nguyen Chau Giang, 2012; Vietnamese Ministry of Education and Training, 2018a; 2018b).
The North Central sub-region (six provinces) includes the entire area to the north of the Hai Van pass (east of Thua Thien Hue province) (Nguyen Chau Giang, 2012; Vietnamese Ministry of Education and Training, 2018a; 2018b). In winter, east- northly monsoons bring moisture from the ocean, creating wet and cold weather for the entire area. This is different from the dry weather in the winter of the northern region of Vietnam. In summer, there is little moisture from the sea, but west-southerly monsoons (also known as Laos wind) blow through the area. This causes hot and dry weather for the land: the temperature in this period can be up to 40oC, while the humidity is very low. In general, the Central Coast region has a cold but short winter (less than 90 days). The mean temperature is 1o–2oC higher than the temperature in the Hong Delta (North Vietnam). The annual mean temperature is 23o–25oC, total sunny hours is 1,460–1,920, the mean rainfall is 1,500–2,500 mm/year (highest in Thua Thien Hue province), and relative humidity is 82–87%. In addition, this region usually encounters abnormal climatic events, as it has the harshest climates compared to other regions of Vietnam. Various natural disasters occur annually due to its location and topography, such as storms, floods, hot and dry breezes from the west, and droughts. This region is seriously affected by typhoons or depressions from the West Pacific, causing storms and floods for the main land in winter. In summer (April–August), after
4 Chapter 1: Introduction
leaving moisture at the west of the Truong Son Range, the west-southerly monsoons produce a hot and dry climate for the region, especially for Nghe An, Ha Tinh, Quang Binh, and Quang Tri provinces (Nguyen Chau Giang, 2012; Vietnamese Ministry of Education and Training, 2018a; 2018b).
The South Central Coast is characterised as a tropical monsoon and partly equatorial climate (Nguyen Chau Giang, 2012; Vietnamese Ministry of Education and Training, 2018a; 2018b). This sub-region has stronger sunlight than its North Central counterpart, and it has lower temperature variation and relatively low precipitation (1,200mm/year). Going from the north to the south of the sub-region, rainfall, rainy days, and humidity decrease, while the mean temperature rises. In addition, this region routinely encounters natural disasters, such as typhoons, floods, and droughts. Sand and salt water frequently move inland due to ocean tides and typhoons. Another important climatic characteristic of this area is that the rainy season and dry season are the opposite to those in the north and south of the country. In the summer, while most of the country has its highest rainfall, the South Central Coast region experiences its driest periods (Nguyen Chau Giang, 2012; Vietnamese Ministry of Education and Training, 2018a; 2018b).
1.2 CONTEXT
Witnessing the important transition of the disease pattern over the past four decades, ischemic heart disease (including AMI) is becoming one of the most serious health issues for Vietnam. Both temperature variation and temperature extremes are hypothesised to possibly induce adverse impacts on AMI morbidity and these hypotheses were tested in this study by using data from three different provinces in the Central Coast of Vietnam for the period of 2008–2015.
An important element of research into the effect of ambient temperature on AMI HAs is to assess the time lag effect. There is evidence that an adverse heat effect is usually acute, while an adverse cold effect is maybe prolonged (J. Huang, Wang, & Yu, 2014; Kovats & Hajat, 2008; Moghadamnia et al., 2017). Moreover, the lag effect is hypothesised to be influenced by health care seeking behaviour of patients and the transfer process from home to hospitals (by personal vehicles or ambulance system) and/or transferring from lower level medical care centres to high-level hospitals equipped for special AMI treatment. Especially in the Vietnamese context, the
Chapter 1: Introduction 5
ambulance system has not been well established and is not widely used by patients, and specific treatment for AMI is only available at provincial and central level hospitals. In order to carefully assess the extent of delay of the temperature effect on AMI HAs, an investigation of the extent of pre-hospital delay due to AMI is required.
Previous studies have demonstrated that treating AMI patients in a timely manner with reperfusion therapy is crucial to avoid clinical complications and death. Previous studies have convincingly proven that reperfusion treatment is most effective if patients are treated within one hour of acute symptom onset, especially with ST- segment elevation myocardial infarction (STEMI) patients (Brokalaki et al., 2011; Diercks, Kontos, Weber, & Amsterdam, 2008; Fibrinolytic Therapy Trialists, 1994; Keeley, Boura, & Grines, 2003; Nallamothu et al., 2007; White & Chew, 2008). In Vietnam, only one study (H. Nguyen, Phan, Ha, Nguyen, & Goldberg, 2018) has examined the extent of pre-hospital delay among adult patients hospitalised with AMI in Hanoi (in the north of Vietnam). The results showed a high proportion of delayed patients; 42% AMI patients delayed their presentation at hospital for more than the 12 hours recommended by the Vietnamese Ministry of Health for reperfusion therapy (Vietnamese Ministry of Health 2013). This current study aims to describe the extent of pre-hospital delay in Central Coast residents between 2008-2015.
1.3 AIMS AND RESEARCH SIGNIFICANCE
1.3.1 Aims and objectives This study aims to elucidate the seasonality of, and associations between, temperature variations/extremes and daily HAs due to AMI among adults living in the Central Coast region of Vietnam from 2008 to 2015. In addition, the study examines the pre-hospital delay period (time interval from having the first sign/symptom of health disorders to visiting the first medical facility for treatment) of hospitalisations due to AMI. Based on these findings several public health strategies are then recommended to minimise and prevent temperature-related health risks in the studied region.
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1.3.1.1 Specific objectives of this research in the Central Coast region of Vietnam: 1. Explore the pre-hospital delay period and its associated factors for adult AMI patients;
2. Examine the long-term trends and seasonality of adult AMI hospital admissions;
3. Assess the short-term effects of ambient temperature on adult AMI hospital admissions;
4. Evaluate the effects of extreme temperature conditions (heat waves and cold spells) on adult AMI hospital admissions.
1.3.2 The significance of the research This research aims to improve understanding of AMI pre-hospital delay, the seasonality of HAs due to AMI, and the impacts of temperature variations and extremes on AMI occurrence. These findings should provide valuable information for health authorities to develop public health strategies to avoid prolonged pre-hospital delays for AMI sufferers, and to anticipate patterns of AMI with respect to variations in ambient temperature. In addition, by identifying the subgroups vulnerable to temperature variation/extremes, findings from this research can help health authorities to target these particular groups in prevention campaigns. The study also provides several recommendations for further related research, especially in the Vietnamese context.
1.4 THESIS OUTLINE
This thesis is comprised of eight chapters. Chapter 1 provided an overview of the thesis, and the background, context, aims, and scope of this study. Chapter 2 contains the literature review, where AMI, temperature, temperature-AMI relationships, and their related aspects are explored in relation to previous studies. Chapter 3 outlines the methodology used in this research and provides a summary of the overall study design and methods used for statistical analysis, describing the studied areas, population, data collection, management, and analysis in detail. The first result chapter, Chapter 4, provides a description of the characteristics of the AMI patients and the extent of the pre-hospital delay time and its associated factors, while Chapter 5 presents an exploration of the trends and seasonality of AMI HAs. Chapters
Chapter 1: Introduction 7
6 and 7 report the main findings of this thesis, in relation to the short-term effects of ambient temperature or extremes (heat waves and cold spells) on HAs due to AMI. Chapter 8 provides general discussion of the findings of the study in terms of the contribution to this research field, and reflects upon the study’s strengths, limitations, and the implications for further research. The thesis concludes with recommendations to reduce the negative effect of temperature extremes on AMI occurrence in the community.
8 Chapter 1: Introduction
Chapter 2: Literature Review
While the effects of weather, and in particular, ambient temperatures on overall mortality and morbidity are well documented, the strength of the evidence base for the effects on AMI is less clear (Bhaskaran et al., 2009b). This literature review consists of six main aspects of the research topic. The first and second sections introduce an overview of AMI and its environmentally related triggers. The third section focuses on a review of studies on temperature and (acute) myocardial infarction, including the mechanism for determining temperature effects on the morbidity of AMI. Following this, the knowledge gaps and research problems are outlined.
2.1 OVERVIEW OF ACUTE MYOCARDIAL INFARCTION
2.1.1 Acute myocardial infarction 2.1.1.1 Definition, types and pathology
Acute myocardial infarction (AMI) with or without ST-segment elevation (STEMI or non-STEMI) is a common cardiac emergency, characterised as the occlusion of a major coronary artery leading to cardiac muscle necrosis (Anderson & Morrow, 2017; Ha, 2010). It is one of the two most common clinical manifestations of ischemic heart diseases (IHDs), while IHDs account for the majority of cardiovascular diseases (CVDs) (Wichmann et al., 2013).
AMI is classified into six types: infarction due to coronary atherothrombosis (type 1), infarction due to a supply-demand mismatch that is not the result of acute atherothrombosis (type 2), infarction causing sudden death without the opportunity for biomarker or ECG confirmation (type 3), infarction related to a percutaneous coronary intervention (PCI) (type 4a), infarction related to thrombosis of a coronary stent (type 4b), and infarction related to coronary artery bypass grafting (type 5) (Thygesen et al., 2012).
The well-known initial mechanism for AMI is rupture or erosion of a vulnerable, lipid‐ laden atherosclerotic coronary plaque, resulting in the exposure of circulating blood to a highly thrombogenic plaque core and matrix materials (Anderson & Morrow, 2017; Thygesen et al., 2012). Platelets adhere, are activated, and aggregate. Thrombin is generated, accelerating platelet activation, and fibrin is formed, trapping red blood cells, and forming a thrombus. A totally occluding thrombus typically leads to ST-segment elevation myocardial infarction
Chapter 2: Literature Review 9
(STEMI), whereas partial occlusion, or occlusion in the presence of collateral circulation, results in non ST-segment elevation myocardial infarction (Non-STEMI) or unstable angina, often with ST‐depression. Ischemia resulting from reduced coronary flow leads to myocardial cell injury or death, ventricular dysfunction, and cardiac arrhythmias. Myocardial necrosis begins after as little as 20 minutes of coronary occlusion. It takes several hours before myocardial necrosis can be identified by macroscopic or microscopic post-mortem examination. Complete necrosis of myocardial cells at risk requires at least two to four hours, or longer, depending on the presence of collateral circulation to the ischaemic zone, persistent or intermittent coronary arterial occlusion, the sensitivity of the myocytes to ischemia, preconditioning, and individual demand for oxygen and nutrients (Anderson & Morrow, 2017; Thygesen et al., 2012). A summary of the main pathological processes that result in AMI is presented in Figure 2.1.
The occurrence of AMI without critical pericardial coronary disease accounts for approximately 10% of AMI and has various mechanisms, though it is primarily due to microvascular disease and endothelial dysfunction. An additional mechanism in Non-STEMI is spontaneously lysis of thrombus on an eroded but non‐obstructive plaque. This mechanism is increasingly relevant in the setting of prompt administration of anticoagulation and antiplatelet therapy to patients on arrival at the emergency department prior to angiography (Anderson & Morrow, 2017).
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Figure 2.1 Pathologic and Clinical ST-Segment Elevation Acute Myocardial Infarction (STEMI) and Non-STEMI Acute Coronary Syndromes
Note: ECG denotes electrocardiogram, MI denotes myocardial infarction, and STEMI denotes ST-segment elevation myocardial infarction. (Source: Anderson & Morrow, 2017)
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2.1.1.2 Acute and Prodromal Myocardial Infarction Symptoms
AMI is a serious health condition and its early treatment is essential for saving lives and preventing severe implications (Nguyen et al., 2018). Therefore, identifying acute and prodromal symptoms of AMI is important for its prevention. As mentioned above, chest pain was the most important and frequently reported symptom. However, the fact that there are AMI patients experienced no chest pain is a concern, especially in women, the elderly and patients with diabetes (Kasper et al., 2015, McSweeney et al., 2012).
The most important typical symptom of AMI is chest discomfort as frank pains (described as heavy, squeezing, and crushing, stabbing or burning) that located in the substernal region and usually radiates to the left arm, left shoulder, or neck. The pain has some features, such as occurring at rest, lasting longer than 10 minutes; and it occurs with a crescendo pattern (i.e., distinctly more severe, prolonged, or frequent than previous pains). It is often accompanied by weakness, sweating, nausea, vomiting, anxiety, and a sense of impending doom. However, chest pain is not always present in patients with AMI. The proportion of painless MIs is greater among people with diabetes or the elderly. Anginal “equivalents” such as breathlessness, dyspnea, stomachache, nausea, or weakness may occur instead of chest pain. Moreover, physical findings, with or without pain, include sudden loss of consciousness, a confusional state, a sensation of profound weakness, temperature elevations etc. (Kasper et al., 2015).
Other acute and prodromal myocardial infarction symptoms should be noted in relation to awareness of AMI onset in population at risk, even though they are unlikely related to this health condition at all. They include indigestion, abdominal pain, legs pain, headache, unusual fear, sleep change, and loss of appetite. Those symptoms have been used to screening for developing progressive Coronary Heart Diseases or myocardial infarction (DeVon et al., 2008; McSweeney et al., 2012; McSweeney et al., 2013; Zimmerman et al., 2016).
Details of AMI diagnosis is presented in Section 3.5.1.1 based on the third universal definition of AMI, sourced by Thygesen et al. (2012).
2.1.2 Global and national burdens of CVDs/IHDs/AMI 2.1.2.1 Global burden of CVDs/IHDs/AMI As mentioned earlier, AMI is one of the two most common clinical manifestations of IHDs, which occupy the majority of CVDs (Wichmann et al., 2013). Thus, AMI is a major social and health issue worldwide because CVDs/IHDs remain the leading causes of morbidity
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and mortality (S. Lee et al., 2014). CVDs are the number one cause of death globally (World Health Organization 2017a). In 2015, CVD mortality accounted for 31% of total deaths around the world, with an estimation of 17.7 million people having died from CVD in this year. Of these deaths, approximately 7.4 million died from coronary heart disease. Over three quarters of CVD deaths were in low and middle-income countries (World Health Organization 2017a). Ischaemic heart disease was the world’s biggest killer, occupying nearly 8.8 million deaths in 2015, accounting for 15.5% of the total deaths worldwide (World Health Organization 2017d). As reported in this document, apart from low-income economies where infectious disease was still the dominant cause of death, IHD was the leading cause of death in all other economies globally.
Moreover, IHD has become the leading contributor to the burden of disease as assessed on the basis of disability-adjusted life-years (DALY) and years of life lost (YLL) (World Health Organization 2017b). The DALY of CVDs accounted for 15% of total DALY worldwide, of which 7.2% of the total was due to IHDs in 2015. Estimations for YLL for CVDs and IHDs were 20% and 9.5%, respectively. These figures show that CVDs, particularly IHDs, were the leading cause of DALYs and YLLs in the most recent global estimations (World Health Organization 2017b).
The epidemiologic characteristics of AMI have changed dramatically over the past three to four decades. Since 1987, the adjusted incidence rate of hospitalisation for AMI or fatal coronary artery disease in the United States has declined by 4–5% per year. Nevertheless, approximately 550,000 first episodes and 200,000 recurrent episodes of AMI occur annually. Globally, AMI has shifted to low- and middle-income countries. In a study on 156,424 individuals in 17 countries who were followed for a mean of 4.1 years, the burden of AMI was directly related to income (Anderson & Morrow, 2017). It showed that the highest burden of risk factors in high-income countries and the lowest burden in low-income countries. In contrast, an inverse relationship with income was noted for rates of AMI (1.92, 2.21, and 4.13 cases per 1000 person-years in high-, middle- and low-income countries, respectively; p < 0.001 for trend) (Anderson & Morrow, 2017). Mitigation of the high burden of risk factors in higher-income countries has been attributed to a greater use of preventive measures and revascularisation procedures (Sanchis-Gomar, Perez- Quilis, Leischik, & Lucia, 2016). In developing countries, including Vietnam, with the development of social-economic conditions (facing their people with more risk factors of CVDs/AMI) but still lack of capacities of preventative and treatment measures, CVDs/AMI is becoming a topical health issue of the countries.
Chapter 2: Literature Review 13
2.1.2.2 National burden of CVDs/IHDs/AMI Vietnam has experienced a significant transition in the disease pattern in the past decades and CVDs are now among the leading causes of mortality, YLLs, and DALYs (Vietnamese Ministry of Health, 2011). In 2014, H. Nguyen et al. reported that CVDs were the leading cause of death in Vietnam, accounting for roughly one quarter of all deaths yearly. The World Health Organization (2017b) reported that CVDs accounted for 32.8% of total deaths in Vietnam, the highest proportion of all causes in 2015. CVDs were also the leading cause of DALY (15.2%) and YLL (20.9%) in that year (World Health Organization 2017b). In addition, CVD mortality is increasing during the last decade (See Figure 2.2) (World Health Organization, 2014a).
Figure 2.2 The probability of dying between ages 30 and 70 years from the four mains Noncommunicable Diseases in Vietnam (Source: World Health Organisation, 2014a)
Regarding subclasses of IHDs, they were the second leading cause of deaths in both males, with 30,800 deaths (10.1%, following stroke–17.5%), and females, with 27,700 thousand deaths (11.6%, following stroke–18.9%) in 2015. The disease was also the third leading causes of DALYs (4.7%) and YLL (6.5%), following stroke and road injuries (World Health Organization 2017b). Among the limited information about AMI available for Vietnam, this disorder was in the top five highest causes of deaths in 2015, with an incidence of 0.84/100,000 population (Ministry of Health 2017). These figures show that CVDs, IHDs, and AMI are topical issues that need to be the focus of more investigations, especially research into their non-traditional risk factors (e.g., air pollution and weather/temperature).
It is worth noticing that, in a low resource setting country like Vietnam, health profiles and relevant data are limited. The NCDs/CVD/IHD/AMI mortality or incidence estimates for
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Vietnam have a high degree of uncertainty because they are sometimes not based on national mortality data, but based on a combination of country life tables, cause of death models, regional cause of death patterns, and WHO and UNAIDS programme estimates for some major causes of death (World Health Organisation, 2014a). Therefore, we supposed that those data underestimate the problem due to under-enumeration and/or misclassification during medical data recording and reporting.
2.1.3 Risks and associated factors of acute myocardial infarction 2.1.3.1 Traditional risk factors The lifetime risk for coronary heart diseases (CHD) varies dramatically depending on the profile of several well‐known risk factors. These include age, gender, lipid profile, blood pressure, diabetes, smoking status, family history, and race (Anderson & Morrow, 2017; Frers, Otero-Losada, Kersberg, Cosentino, & Capani, 2017; A. Long, B. Long, & Koyfman, 2018). When all modifiable risk factors are optimal, the lifetime risk of CHD for a 45 year‐old female is estimated to be <5%, whereas with ≥ 2 major risk factors, it is 50% for men and 31% for women (Anderson & Morrow, 2017).
Specific to AMI, the INTERHEART Study, a global case‐control study across 52 countries and including 15,152 incident cases of AMI and 14,820 controls, identified nine readily measured risk factors (smoking, lipids, hypertension, diabetes, obesity, diet, physical activity, alcohol consumption, and psychosocial factors) that accounted for over 90% of the population attributable risk of AMI (S. Yusuf et al., 2004). Importantly, these risk factors were the same in almost every geographic region and racial/ethnic group worldwide and were similar for men and women (S. Yusuf et al., 2004). The genetic contribution to AMI is less clear, and evidence for genetic variants predisposing specifically to the precipitation of AMI is even weaker (Anderson & Morrow, 2017).
The recent national STEPS 2015 survey (STEPwise approach for non-communicable disease risk factor surveillance in 2015 for 18–64 years old group) showed that most of the risk factors were quite common in the Vietnamese population (Department of preventive medicine- Vietnamese Ministry of Health, 2016). An estimated 77.3% of males and 11.0% of females consumed alcohol, and in particular, 44.2% of males and 1.2% of females consumed alcohol at a risk level (having six or more standard alcohol drinks during the past 30 days, defined by WHO). An estimated 22.5% of the population smoked, this proportion for males and females was 45.3% and 1.1%. More than half of adults (57.2%) consumed less vegetables and fruits
Chapter 2: Literature Review 15
than recommended by the WHO and around one third of this population lacked physical activity, with 20.2% in males and 35.7% in females. The proportion of population that were overweight was 15.6%. While two common risk factors for AMI decreased in the Vietnamese population between 2010 and 2015 (i.e., low consumption of fruit and vegetables, low physical activity levels), other risk factors that were examined did not reduce or significantly increase, including alcohol consumption, obesity rates, and blood cholesterol levels. Compared to STEPS 2010, in the 24-64 years old group, the proportion of the population with current alcohol consumption was higher (44.8% vs 37.0%), similar to the proportion of overweight people (17.5% vs 12%) (Department of preventive medicine-Vietnamese Ministry of Health, 2016). More detailed results from STEPS 2015 and STEPS 2010 are presented in Appendix F.
Regarding tobacco smoking, results from the Global Adult Tobacco Surveys (GATS) (Viet Nam Steering Committee on Smoking and Health, World Health Organization, Ha Noi Medical University, & Center for Disease Control 2010), for those 15 year old and older, showed a declining trend of prevalence of tobacco smoking in Vietnam (Van Minh et al., 2017). The Vietnam GATS found that the prevalence of current smokers in Vietnam was 22.5% (vs 23.8%) overall, 45.3% (vs 47.7%) among men, and 1.1% (vs 1.4%) among women in 2015 and 2010, respectively (World Health Organization, 2015a, 2015b). In another study in Vietnam for 25-64 years old groups, Bui et al. (2015) found that the prevalence of current daily smoking had declined in the most recent birth cohorts of men and had declined across almost all cohorts of women. The declines in tobacco smoking are possibly in response to anti-tobacco initiatives commencing in the 1990s (Bui et al., 2015).
There is limited information regarding risk factors for CVDs/AMI in different parts of Vietnam, especially for sub-regional comparisons between North and South-Central Coast areas. Most national surveys tend to group the whole region as one “Central Coastal region” that is generally assumed to be homogenous in terms of demographic characteristics (but not the climate) compared to other parts of Vietnam. However, three studied provinces in this research (namely Quang Binh, Thua Thien Hue province have several differences in characteristics that may contribute to variance in risk factors. Khanh Hoa has higher social- economic conditions than the others, including higher average income per capita, lower poverty rate and more urbanisation (See more details about the differences in Section 3.2 and Appendix G). As a consequence, it might face more negative lifestyle-related health effects in terms of CVDs/AMI because Vietnamese national statistics show that there is higher prevalence of overweight/obesity, hypertension, high blood glucose, unhealthy diet physical inactivity in
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people living in urban areas versus people living in rural areas (Vietnamese Ministry of Health, 2015).
2.1.3.2 Non-traditional risk factors The literature has identified a number of other risk factors for CVDs and atherosclerosis, in addition to the traditional ones noted above. This is especially the case in relation to premature atherosclerosis. Such factors include chronic kidney disease, systematic lupus erythematosus, rheumatoid arthritis, Hodgkin’s lymphoma, and cancer treatments, for example radiation therapy and chemotherapy (Long et al., 2018). A greater risk of AMI is also associated with heavy alcohol use and pregnancy. Atherosclerosis and CVDs show a greater incidence among individuals infected with HIV, as well as those being treated with antiretrovirals. Furthermore, the inflammation caused by systemic infections such as pneumonia and sepsis has been linked with ACS events, both at the time the infection is present and in the 12 months following its diagnosis (Long et al., 2018).
It is essential for physicians assessing a patient with potential ACS to not only identify the traditional risk factors for atherosclerosis and CVDs, but to also find other non-traditional factors in their evaluation that may increase the risk of CVDs. Knowledge about these independent factors for atherosclerosis is important to minimise the misdiagnosis of ACS (Long et al., 2018).
In light of global climate change, there is increasing interest in the effect of meteorological factors and air pollution on health outcomes, especially CVDs in general, and AMI in particular (Danet et al., 1999; Escamilla-Cejudo, 1999; Massimo Franchini & Mannucci, 2015; J. Yang et al., 2017). Although the findings were inconsistent, a number of researchers have found significant associations between several weather factors and air pollutants and AMI mortality and morbidity (Fernández-García, Díaz, Hidalgo, Fernández, & Sánchez-Santos, 2015; Goggins et al., 2013; Honda et al., 2016; Sheth, Nair, Muller, & Yusuf, 1999; Yamaji et al., 2018). More details about these relationships are presented in Section 2.2.
2.1.4 Seasonality of acute myocardial infarction Seasonal variations in myocardial infarction mortality and morbidity rates have been investigated over a large range of latitudes, and their death and occurrence rates are generally higher in winter than other seasons (Khan & Halder, 2014; J. H. Lee et al., 2010; Radišauskas et al., 2014; Sheth et al., 1999; Shuie, Perkins, & Bearman, 2016; J. Yang et al., 2017). However, there are still inconsistent findings regarding the seasonal variation across regions
Chapter 2: Literature Review 17
and populations. Several studies implemented in Taiwan (Ku et al., 2008), Spain (Fernández- García et al., 2015), and Slovenia (Ravljen et al., 2018) found no seasonal variations at the onset of AMI. For those who found a significant winter peak of AMI morbidity and mortality rates, the effect was explained by a multi-factorial mechanism. In winter, the cold conditions stimulate sympathetic nervous activity and greater sodium intake, resulting in higher blood pressure, heart rate, and left ventricular end-diastolic pressure and volume (Hiramatsu, Yamada, & Katakura, 1985; Houdas, Deklunder, & Lecroart 1992; Modesti et al., 2006). Consequently, the heart shows an increased need for oxygen and lower ischemic threshold (Houdas et al., 1992). Individuals with an already-compromised coronary circulation may be particularly impacted by these. Such patients may also suffer more severe events, including sudden death due to more cardiac arrhythmias, or through higher blood pressure, or rupture of atherosclerotic plaques (Houdas et al., 1992). A greater tendency for clotting has been demonstrated in the circulatory system in winter (De Lorenzo, Kadziola, Mukherjee, Saba, & Kakkar, 1999; Eldwood et al., 1993; Ockene et al., 2004; Stoll & Bendszus, 2006). This could be related to plasma volume contraction (haemo-concentration) (Ockene et al., 2004; De Lorenzo et al., 1999; Hassi, Rintamäki, Ruskoaho, Leppäluotot, & Vuolteenaho, 1991; Wolf et al., 2009).
2.1.5 Early treatment of acute myocardial infarction and the importance of in-time hospital arrival Pre-hospital cardiac arrest and extension of necrosis are major factors in AMI-associated death and heart implications, making quick primary assessment, initial treatment, and transportation to a hospital essential determinants of initial care (Anderson & Morrow, 2017). When AMI patients arrive at hospitals, early hospital care includes bed rest; oxygen supplement; administration of analgesics, nitrates, beta-blockers, and calcium-channel blockers; anti-platelet therapy; anticoagulant therapy; statin therapy; and an angiotensin- converting-enzyme inhibitor. Among these initial management approaches, physicians at the emergency department need to apply the appropriate approaches based on the patient’s condition and their prognosis. For example, oxygen supplementation is only recommended for patients with hypoxemia, while beta-blockers administration is used for patients at risk of cardiogenic shock (Anderson & Morrow, 2017).
Specific management algorithms for STEMI and Non-STEMI are slightly different based on the residual perfusion in the ischemic zone in Non-STEMI (Anderson & Morrow, 2017; Taghaddosi, Dianati, Bidgoli, & Bahonaran, 2010). However, this section only discusses the treatment for STEMI, acknowledging that the urgency and approach to revascularisation differs
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from that in Non-STEMI. Emergency reperfusion of ischemic myocardium zone that is becoming infarcted is the essential therapeutic goal. There are two approaches to achieve this goal, including an interventional procedure and intravenous fibrinolytic therapy. While intravenous fibrinolytic therapy is involved in the administration of anti-platelet agents and anticoagulant agents recommended at the time of first medical contact, the interventional procedure (either coronary angiography, percutaneous coronary intervention (PCI) or coronary artery bypass grafting (CABG)) is only implemented at hospitals that have a catheterisation laboratory (PCI–capable hospitals) (Anderson & Morrow, 2017; Taghaddosi et al., 2010). PCI is the preferred approach for STEMI with the onset of symptoms within the previous 12 hours at PCI-capable hospitals due to its advantages of lower rates of early death, re-infarction, and intracranial hemorrhage compared to fibrinolytic therapy (Anderson & Morrow, 2017; Taghaddosi et al., 2010). Nevertheless, when PCI is delayed by more than 120 minutes, fibrinolysis should be provided if it is not contraindicated, taking the consideration of transfer within 24 hours to a PCI-capable medical centre. Thirty-day mortality rates have progressively decreased from more than 20% to less than 5% with the broad application of reperfusion approaches for STEMI 30 (Anderson & Morrow, 2017; Taghaddosi et al., 2010).
It is estimated that approximately 40% to 60% of AMI deaths occur within the first hour after the appearance of the first symptom of the disorder (Taghaddosi et al., 2010). Early restoration of coronary flow by reperfusion approaches significantly reduces mortality by limiting cardiac damage and preventing the development of complications (Brokalaki et al., 2011; Momeni, Salari, Shafighnia, Ghanbari, & Mirbolouk, 2012). These studies have shown that the efficacy of thrombolysis depends directly on the time between AMI onset and the provision of thrombolytic medication. The survival rate is very high when treatment is initiated within an hour of the onset and is dramatically reduced after six hours. It has been shown that the survival rate is approximately 50% if the thrombolytic treatment is initiated within one hour and is decreased to 28% when provided three hours after the appearance of first symptom of AMI (Fibrinolytic Therapy Trialists, 1994; Nallamothu et al., 2007). Moreover, an early interventional procedure is superior to thrombolysis in the medium-and long-term viability of the patient when provided within the first 90 min of the episode; however, this advantage disappears after the first 180 min (Brokalaki et al., 2011; Diercks et al., 2008; Keeley et al., 2003; White & Chew, 2008). Therefore, shortening the pre-hospital period from the appearance of the first symptom to hospital presentation in order to obtain an early diagnosis and
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reperfusion treatment can prevent life threatening consequences and complications of AMI patients.
A number of recent studies on delayed hospital arrival by AMI patients worldwide (i.e. in Greece, Switzerland, USA, Iran, Hong Kong – China, Korea and Vietnam) have shown the median delay to hospitalisation ranges from two to eight hours (Ängerud, Sederholm Lawesson, Isaksson, Thylén, & Swahn, 2017; Dianati, Mosavi, Hajibagheri, & Alavi, 2010; Brokalaki et al., 2011; Momeni et al., 2012; M. R. Lee et al., 2016; Makam et al., 2016; P. W. Li & Yu, 2017; H. Nguyen et al., 2018). The only study implemented in Vietnam on this topic revealed that the median delay to hospital arrival was 2.8 hours, whereas the mean time was 14.9 hours (H. Nguyen et al., 2018). Both these results were higher than recommended time for efficacious treatment. It is important to differentiate the delay time from first symptoms to hospital presentation; the time from first symptoms to receiving reperfusion treatment and the time from presentation to receiving reperfusion treatment. Different factors may be associated with these time periods. The first mentioned period is used in this study as the estimate of “delay time” but this is not the same as the time to access “efficacious treatment” (Dauerman and Sobel, 2005).
2.2 ENVIRONMENTAL RELATED FACTORS
2.2.1 Ambient temperature 2.2.1.1 Temperature extremes Temperature impacts on human mortality and morbidity have been investigated over the last few decades (Basu & Samet, 2002; Turner, Barnett, Connell, & Tong, 2012; Ye et al., 2012). Evidence shows that this is a significant public health issue (Basu & Samet, 2002). Observation of heat and mortality has been reported since the early 20th century. Cardiovascular, respiratory, and cerebrovascular diseases are commonly reported as underlying causes of heat-related death (Basu & Samet, 2002). In addition, an increase in hospitalisations is associated with exposure to extreme temperatures (heat waves and cold spells) (Lim, Hong, & Kim, 2012; Linares & Díaz, 2008; Loughnan et al., 2010b; Pudpong & Hajat, 2011; Radišauskas, Vaičiulis, Ustinavičienė, & Bernotienė, 2013; Tong, Wang, & Guo, 2012; Wichmann, Ketzel, Ellermann, & Loft, 2012; Wichmann et al., 2013). Admissions for heat stroke, heat exhaustion, fluid and electrolyte abnormalities, and acute renal failure are higher during heat waves than non-heat wave periods. In winter, frostbite and hyperthermia, ischemic
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stroke, coronary events, and cardiovascular and respiratory diseases often increase (Ye et al., 2012).
2.2.1.2 Temperature variation Even moderate deviations from temperature norms can induce risks to the population (Egondi, Kyobutungi, & Rocklöv, 2015). Temperature variability is an important factor explaining differences in temperature-related mortalities across regions. There is evidence that when the temperature changes considerably over the course of a few days, even without reaching extremes, it is harmful for vulnerable populations, especially those with existing health problems. In addition, there is evidence of adaptation to temperature extremes, due to the fact that cold and heat effects seem to have different thresholds for increased risk onset for different regions (Egondi et al., 2015).
In order to establish effective approaches to mitigate or reduce temperature impacts on health, further research focusing on specific diseases and locations is required. Each region, country, or area has different climate and socio-economic conditions. Several domestic and local adaptation factors (e.g., use of air conditioning) could influence the direction and magnitude of the effects of ambient temperature on health outcomes (Wichmann et al., 2013). Experts on climate change and its impacts agree that health risks of climate change vary across the globe, and it is therefore necessary to evaluate the impacts at local, national, and global levels (Adshead, Griffiths, Rao, & Thorpe, 2009; Balbus et al., 2013).
Details of studies on the impact of temperature variation/extremes on AMI hospitalisation and potential mechanisms for the effects are provided in Section 2.3.
2.2.2 Other meteorological factors 2.2.2.1 Humidity There is limited information regarding the association between humidity and AMI morbidity. Honda et al. (2016) found that on the days where more than two AMI patients were admitted at the studied centre, the mean humidity significantly decreased compared with days that had less than two AMI patients admitted. In constrast, other studies have reported an association between a 1% increase in relative humidity and a 2% increase in acute coronary syndrome admissions (Aylin et al., 2001; Panagiotakos et al., 2004). Similarly, Panagiotakos et al. (2004) and Abrignani et al. (2012) reported a positive association between relative humidity and hospital admissions (HAs) for acute coronary syndromes, including angina.
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One could hypothesise that when air has a high percentage of humidity, perspiration and the processes of temperature homeostasis might be hindered, making the automatic processes of internal temperature control more difficult, and inducing respiratory fatigue and heart rate (Abrignani et al., 2009; Tian et al., 2016). However, this mechanism may only be important in more severe ischemic forms (Abrignani et al., 2012).
2.2.2.2 Air pressure The consequences of atmospheric pressure on AMI morbidity have increasingly been a topic of research interest. However, there are still inconsistent conclusions about whether low or high atmospheric pressure is associated with an increase in AMI frequency (Honda et al., 2016; Spencer, Goldberg, Becker, & Gore, 1998; Verberkmoes et al., 2012). In a recent study in Serbia (Bijelović et al., 2017), the results showed that lower air pressure (< 1009 hPa) adjusted for the temperature and relative humidity were good predictors of the lower AMI incidence among the examined population (the adults and the elderly). Yet, according to the available literature (Goerre et al., 2007; Radišauskas et al., 2013), consequences of air pressure on the AMI have not been studied very often at a daily level. One of the studies that examined the daily effects of air pressure on AMI events reported that a higher occurrence of AMI is related to atmospheric pressure below 1000 hPa (Sarna, Romo, & Siltanen, 1977). Data from a study conducted in Texas showed a significant relationship between air pressure and the occurrence of AMI, where the decrease in air pressure for 1.0 IH/h increased the odds of having AMI the next day by 10% (Houck, Lethen, Riggs, Gantt, & Dehmer, 2005). Similarly, in the MONICA project, a 10-year survey of 257,000 patients with myocardial infarction and coronary deaths, a V-shaped relationship was found between atmospheric pressure and the rate of events (Danet et al., 1999).
Houck et al. (2005) proposed that variations in atmospheric pressure might increase the risk of plaque rupture. According to engineering principles, a structure fails via the application of an external force great enough to overcome its mechanical properties (Houck et al., 2005). This can be demonstrated using a bathysphere, whose structural integrity fails when it is ascending (decrease in pressure) rather than descending. Thus, an estimation of the mechanical forces exerted on an atherosclerrotic plaque can be calculated by the mathematical equation for wall stress in a thin-walled cylinder. (That is: wall stress _ PR/T, where P is the pressure within the cylinder or plaque, R is the radius of the cylinder or plaque, and T is the wall thickness of the cylinder or fibrouscap). According to this equation, wall stress will increase with the size of the plaque but decrease as the fibrous cap thickens. The equation applies to static conditions;
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thus, blood pressure fluctuations and coronary arterial movements during the cardiac cycle will result in further changes. Changes in pressure associated with the weather could produce additional dynamic forces, resulting in earlier structural failure. This could also result from changes in the plaque’s structure at the site that vessels from the vasorum or from neovascularisation penetrate it. Houck et al. (2005) also explained that the V-shape or U-shape was due to the fact that distribution of air pressure changes consecutively on a daily level (the decrease in air pressure on one day is often associated with the increase in air pressure on the next), known as a low-pressure weather front passing through the area that might contribute to the plaque rupture.
2.2.2.3 Wind speed, sunlight hour and rainfall Several studies have investigated the effect of wind speed, sunlight, and rainfall on the onset of AMI (Abrignani et al., 2012; Honda et al., 2016); however, the effects were not proven to be as strong or clear as other meteorological factors, such as temperature, humidity, and air pressure. This suggests that these variables are weak ischemic triggers (Abrignani et al., 2012; Abrignani et al., 2009; Crawford, McCann, & Stout, 2003; J. H. Lee et al., 2010).
2.2.3 Air pollution Air pollution can consist of particulate matter (PM) and gaseous pollutants (i.e., carbon monoxide, nitrogen dioxide, sulphur dioxide, and ozone) in the air (Sen et al., 2016). High levels of air pollution are a leading environmental problem, especially in developing countries. Lelieveld et al. (2015) calculated that outdoor air pollution leads to 3.3 (95%CI:1.61–4.81) million premature deaths per year worldwide, predominantly in Asia (Lelieveld et al., 2015). Many studies have shown that air pollution increases the risk of acute cardiovascular events, specifically AMI (Brook et al., 2004; Franchini & Mannucci, 2007; Franchini & Mannucci, 2009; Hoek, Brunekreef, Fischer, & Van Wijnen, 2001; Pope et al., 2004). In 2009, a detailed systematic review by Bhaskaran et al. about the effect of air pollution on the incidence of acute AMI showed the association between air pollution and the risk of AMI (Bhaskaran et al., 2009a). In their study including 691 patients, Peters et al. (2004) found that acute exposure to air pollution within the preceding two hours was related to AMI. More recently, Gardner et al. (2014) found that fine particles were associated with an increased risk of STEMI but not Non- STEMI, and interpreted this result as that PM might impair the balance between thrombosis and endogenous thrombolysis in favour of thrombosis, therefore leading to total coronary occlusion and STEMI instead of Non-STEMI (Gardner et al., 2014).
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There are many possible mechanisms by which PM causes cardiovascular events. One possible mechanism is the direct effect of very small particles (2.5µm) by translocating the systemic circulation through distal lung parenchyma. In systemic circulation, these nano- particles may lead to cardiovascular events by inducing systemic inflammation, thrombosis, activation of vascular endothelium, and destabilisation of atherosclerotic plaques (Franchini & Mannucci, 2011; Nemmar et al., 2002; Nemmar et al., 2001; Sen et al., 2016; Shrey, Suchit, Deepika, Shruti, & Vibha, 2011).
Inhaled particulate matter may induce the release of proinflammatory cytokines such as interleukin-6, interleukin-1β, tumour necrosis factor–α, circulation, and interferon-γ into the circulation by interacting with pulmonary parenchymalmacrophages and bronchial epithelial cells. The release of these proinflammatory mediators into the systemic circulation cause prothrombotic effects by increasing the expression of tissue factor, fibrinogen, and factor VIII and inducing platelet aggregation (Franchini & Mannucci, 2011; Fujii et al., 2002; Ghio, Kim, & Devlin, 2000; Peters, Döring, Wichmann, & Koenig, 1997; Shrey et al., 2011). Particulate matter may also stimulate lung receptors of the autonomic nervous system in favour of a sympathetic system. This sympathetic overtone may lead to vasoconstriction, hypertension, and arrhythmias (Rhoden, Wellenius, Ghelfi, Lawrence, & González-Flecha, 2005). Particulate matter triggers reactive oxygen radicals in both a direct and indirect manner in pulmonary and vascular tissue (Li et al., 2006; Sen et al., 2016).
2.2.4 Influenza Acute infections, including influenza, are also risk factors for premature atherosclerosis and CVDs, specifically AMI (Clayton, Thompson, & Meade, 2007; Corrales-Medina, Madjid, & Musher, 2010; Ramirez et al., 2008; Smeeth et al., 2004; Warren-Gash, Smeeth, & Hayward, 2009). Several observational studies have demonstrated an association between influenza and AMI, and a systematic review of these studies recommended influenza vaccination in order to reduce AMI (Warren-Gash et al., 2009). A study in the United Kingdom was designed to determine whether AMI was associated with the administration of influenza, tetanus, or pneumococcal vaccinations (Smeeth et al., 2004); however, the investigators did not find an associated risk of AMI with vaccinations, but they did find that rates of CVDs (AMI and stroke) were higher in patients with acute respiratory infections and urinary tract infections. Infection can induce ischemia and increase inflammation, leading to atherosclerotic plaques, endothelial dysfunction, and pro-coagulant changes in the patient's blood (Corrales-Medina et al., 2010; Long et al., 2018).
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2.3 REVIEW OF STUDIES ON AMBIENT TEMPERATURE AND ACUTE MYOCARDIAL INFARCTION
2.3.1 Temperature variation/extremes effects Bhaskaran et al. (2009) conducted a systematic review of the specific impacts of ambient temperatures on myocardial infarction. This review included 19 selected articles published during the period between 1991 to 2006. This was the first systematic review to specifically focus on the effects of this environmental stressor on the incidence of MI, describing relationships, including effect sizes, populations studied, location and setting, ascertainment of MI events, potential confounders, and the consideration of delayed effect. The authors concluded that both hot and cold weather had adverse effects on the short-term risk of MI (i.e., eight studies reported a statistically significantly increased risk of MI at lower temperatures, while seven studies revealed increases in risk at higher temperatures using relevant data analysis). Moreover, this review article showed that the majority of studies reported main effects on the same day or up to three days later. None of the studies reported substantial effects after one week (Bhaskaran et al., 2009b).
In light of this association, peer-reviewed articles of studies on temperature and AMI morbidity from 2006 until March 2018 were selected for inclusion in the review. PubMed, Scopus and Google scholar were used to search for the following keywords: ambient temperature, acute myocardial infarction and hospital admission. Synonyms for ambient temperature included air temperature, surrounding temperature, atmospheric temperature, apparent temperature, weather, climate, climatic element, heat, heat extreme, heat wave, heat- related exposure, cold surge, cold spell. Relevant synonymous terms for acute myocardial infarction included: heart attack, myocardial infarct, ischemic heart disease, coronary event, Q wave infarction, STEMI, Non-STEMI, Non-STEACS, coronary infarction, myocardial thrombosis, coronary thrombosis. To limit the literature to focus on hospital admission only, we added these following terms in the search: hospitalisation, presentation (hospital presentation), emergency medical services, emergency (department) visits and morbidity. The search was limited to the English language. Studies that reported mortality counts or excess deaths were excluded so that the focus remained on ambient temperature and morbidity in a variety of locations. Consequently, we obtained 26 articles that met all the criteria above.
Table 2.1 presents 26 studies regarding temperature and AMI HAs published over the last decade. Overall, seasonal variations were noticed for AMI HAs, characterised by a winter peak and summer trough, or a significant negative correlation between temperature and daily
Chapter 2: Literature Review 25
admissions. However, it was difficult to compare the results of these studies because they used various approaches for measuring temperature exposure (i.e., an inter-quartile range (IQR), different temperature thresholds, and number of days for cumulative mean of temperature).
Using a temperature threshold, Shaposhnikov, Revich, Gurfinkel, and Naumova (2014) assessed the relationship between weather and myocardial infarction in two hospitals in Moscow, Russia, in 1992–2005, and found a U-shaped association between temperature (calculation of meteorological data was unclear) and AMI HAs, with the highest amount of admissions at 0oC. In multiple hospitals in Kaunas, Lithuania, in 2000–2007, a decrease in atmospheric temperature (the calculation of meteorological data was unclear) by 10°C reduced the risk of AMI by 8.7 % in the age groups of 45–64 and 65 years and older, and by 19% in the age group of 25 years and older (Radišauskas et al., 2013). In all hospitals in Hong Kong, China, in 2000–2009, a 1°C drop below a threshold temperature of 24°C was significantly associated (p < 0.001) with AMI hospitalisation increases of 3.7% (average lag 0–13 temperature), although the calculation of meteorological data was unclear (Goggins et al., 2013). In all hospitals in Taipei and Kaohsiung, Taiwan between 2000–2009, a 1°C drop below a threshold temperature of 24°C was significantly (p < 0.001) associated with AMI hospitalisation increases of 2.6% (average lag 0–15) and 4.0% (average lag 0–11), respectively (Goggins et al., 2013). Another study in four university hospitals in Daegu City, Korea between 2005–2007 (J. H. Lee et al., 2010), found that a 5°C decrease in mean air temperature was associated with a 5.6% increase of HAs (calculation of meteorological data was unclear).
Other studies have measured temperature exposure by the inter-quartile range (IQR) with various numbers of days for cumulative average of exposure. In all hospitals registered in Gothenburg, Sweden, between 1985–2010, a linear exposure response corresponding to a 3% to 7% decrease in AMI hospitalisations was observed for an IQR increase in the 2-day cumulative average of temperature (Wichmann et al., 2013). Meteorological data from the monitoring station was located on the roof of a 25m high building in the centre of Gothenburg during the entire year (11°C) and the warm period (6°C), respectively ( Wichmann et al., 2013). In a community-wide study in the Worcester area of the USA between 1995–2003, a decrease in IQR in apparent temperature was associated with an increased risk of AMI on the same day (hazard ratio = 1.15, 95% confidence interval: 1.01–1.31), and extreme cold during the prior two days was associated with an increased risk of AMI (1.36, 1.07–1.74) (Madrigano et al., 2013). In a randomised trial in the Catharina Hospital located in Eindhoven, in the Netherlands, in 2006–2008, no association was found between the weather and AMI hospitalisation
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(Wijnbergen, Van’t Veer, Pijls, & Tijssen, 2012). In all hospitals registered in Copenhagen, Denmark, in 1999–2006, for an inter-quartile range (6 or 7°C) increase in the 5-day cumulative average of max temperature (meteorological data from single monitor at Aarhus University), a 4% (95%CI: −2%–10%) and 9% (95%CI: 3%–14%) decrease in the AMI admission rate was observed in the warm and cold periods (Wichmann et al., 2012). In contrast to previous studies, Amiya et al. (2009) only used intra-day temperature in a study of all hospitals registered in Kagoshima, Japan, between 2000–2004, and found that intra-day temperature differences were associated with days of frequent onset AMI (meteorological data from weather statistics in Kagoshima city) (Amiya et al., 2009).
In terms of research methodology, the majority of studies applied a time-series analysis with Poisson regression, controlling for days of week, holidays, and seasonal and long-term trends (K. Bhaskaran et al., 2010; Goggins et al., 2013; J. H. Lee et al., 2010; S. Lee et al., 2014; Shaposhnikov, Revich, Gurfinkel, & Naumova, 2014; Tian et al., 2016; Vasconcelos et al., 2013; Wolf et al., 2009). Generalised linear models (Bhaskaran et al., 2010; Bijelović et al., 2017; Loughnan, Tapper, & Loughnan, 2014; Loughnan et al., 2010a; Shaposhnikov et al., 2014) and Poisson generalised additive models were the most common choices for statistical analyses in these studies (Goggins et al., 2013; Kwon et al., 2015; J. H. Lee et al., 2010; S. Lee et al., 2014; Vasconcelos et al., 2013; Wolf et al., 2009). Recently, Distributed Lag Non-linear models (DLNMs) (Gasparrini et al., 2010a) have been used widely among environmental epidemiologists. By unifying many previous methods into one unique framework, DLNMs are flexible enough to describe non-linear dependencies and delayed effects of exposure at the same time (Yang et al., 2012). More precisely, the modelling is not only used to assess the association between health outcomes on a given day to the exposure levels to predictors on that day, but also to explore the association between the outcome on a given day and exposure to predictors on previous days (Bhaskaran, Gasparrini, Hajat, Smeeth, & Armstrong, 2013; Gasparrini et al., 2010a; Gasparrini & Armstrong 2015). One alternative is the Case-crossover analysis (Gordis, 2009; Tong et al., 2012). The strength of this method is self-matching of cases, eliminating the effects of seasonality, long-term trends, and other confounders from individual time-invariant characteristics (Levy et al., 2001; Lumley & Levy, 2000). However, a limitation of this analytical approach is its complex coding needed to evaluate the main effect between predictor and health outcome in relation to other time-variant variables and their delayed effects. Therefore, DLNM modelling is most appropriate for our research.
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Regarding the temperature indices used, daily mean and average temperatures have been widely used and recognised as the best indicators for estimating exposure (Loughnan et al., 2010b; Misailidou et al., 2006); more composite weather parameters, such as air-mass types, apparent temperature, thermo-hydrological index, and human-biometerological index (physiologically equivalent temperature [PET] generated from air temperature, humidity, wind speed, cloud cover, radiation flux and vapour pressure) have also been selected to assess the effect (J. H. Lee et al., 2010; Madrigano et al., 2013; Morabito et al., 2006; Shuie et al., 2016; Vasconcelos et al., 2013; Wolf et al., 2009).
In addition, several studies have collected more information from AMI cases, trying to build a better understanding of the relationship. For example, in addition to using age and gender as the main strata for usual subgroup analysis, several studies also evaluated other demographic and medical history characteristics, such as the severity of MI; history of hypertension; previous cardiovascular events and diabetes; MI status, including STEMI and non-STEMI; smoking status; and hospital complications of AMI (Kwon et al., 2015; J. H. Lee et al., 2010; S. Lee et al., 2014; Loughnan et al., 2010b; Madrigano et al., 2013). Many other studies have assessed the impact of various meteorological factors and air pollutants on admissions due to AMI (Honda et al., 2016; Mohammadi et al., 2018; Ravljen et al., 2018).
The majority of previous studies have reported a greater AMI incidence triggered by low and/or high temperature, commonly with similar seasonal trends with the peak in winter. However, some researchers have also reported no temperature effects on or seasonality of AMI incidence (Fernández-García et al., 2015; Ravljen et al., 2018; Wijnbergen et al., 2012). This effect has also been detected in different studied subgroups. While low temperature was commonly proven to increase the risk of AMI HAs amongst females, those with a previous history of CVDs, and low socio-economic status (SES) groups (Bhaskaran et al., 2010; Kwon et al., 2015; J. H. Lee et al., 2010); males and younger age groups were at risk of AMI HAs due to high temperature exposure (Mohammadi et al., 2018). The elderly and low SES groups were found to be at risk of both low and high temperature exposure (Madrigano et al., 2013; Misailidou et al., 2006). Diverse methodological analyses were conducted in relation to temperature and AMI HAs and included patients from different geographical regions, confirming immediate and delayed effects. The reported delayed effects varied from a few days (Honda et al., 2016; Wichmann et al., 2012) to up to one week (Shaposhnikov et al., 2014) or even a month (Kwon et al., 2015; Mohammadi et al., 2018). The link between AMI incidence and exposure to other meteorological factors is more contradictory. The differing results
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amongst the studies investigating the associations between environmental factors and heart disease could be contemplated, not only as a consequence of different methodological approaches (Xun, Khan, Michael, & Vineis, 2010), but also as a result of different climate areas, the ability of the human body to acclimatise, and adaptive behavioural patterns (Bijelović et al., 2017; Vardoulakis et al., 2014).
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Table 2.1 Characteristics of studies on ambient temperature and AMI HAs No. Study Location and Research Main Sample size Key findings Comments Vulnerable Time Design and temperature Effect estimates group Statistical exposure analysis variables 1 Misailidou Rural Greek Poisson Daily mean, 1,608 AMI hospitalised -Significant associations between -Controlled: day-of- The elderly et al., 2006 regions regression minimum temperatures and HA rates of week, holidays and the 2003–2004 and AMI. A 18oC decrease in Monday effect. maximum air temperature there was a 1.6% - Stratified for sex, temperatures (95% CI: 0.9–2.2%) increase in age. admissions. - Mean temperature was best fit.
2 Bhaskaran et Conurbations generalised Daily mean 84,010 MI hospitalised of -Without a temperature threshold: -Controlled: People aged al., 2010 in England and linear model temperature, the MINAP project. each 1°C reduction in daily mean seasonality and long- 75-84, pre- Wales with Poisson daily temperature was associated with a term trend, day of the history with 2003–2006 error structure minimum 2.0% (95CI: 1.1% - 2.9%). week and public CVDs, less and - Heat had no detrimental effect. holidays, levels of effect for maximum influenza and those taking temperature, respiratory syncytial aspirin temperature viruses, PM10, O3, at 9 am and daily relative humidity 3pm, and - Lagged 28 days. dewpoint temperature.
3 J. Lee et al., Daegu Generalised Daily mean, 2,136 AMI hospitalised -Significant negative correlation -Controlled: day-of- Female and 2010 city, Korea additive maximum between daily HAs for AMI and week, season, and younger age 2005–2007 Poisson and the mean air temperature. holiday. groups in minimum - Seasonal variations were noted - Stratified for sex, winter. temperature, for AMI, characterized by winter age. diurnal peak and summer trough temperature (p<0.001). range and a - A 5 °C decrease in mean air 30 hermos- temperature was associated with a hydrological 5.6% increase of HAs (β =−0.055, index. RR = 1.056, p < 0.001).
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4 Goggins et Hong Kong, Poisson Daily mean 84,328 AMI hospitalised -Cool temperatures substantially -Controlled: day of the al., 2013 Taipei, generalised temperature raised AMI risk in these warm- week, long-term trends Kaohsiung, additive climate cities. and seasonal effects. 2000–2009 models - Hospitalisation increases of -Adjusted: humidity, 3.7% (average lag 0–13 and PM10, NO2, SO2, temperature) in Hong Kong, 2.6% O3. (average lag 0–15) in Taipei, and - Lagged 28 days. 4.0% (average lag 0–11) in Kaohsiung. - Separate analysis of incident and recurrent cases did show substantial differences with cool temperature effects below 24 °C being considerably stronger for incident cases for all three cities. - A 1°C drop below a threshold temperature of 24 °C was significantly (p <0.001) associated with AMI.
5 Shaposhnik- Moscow, Generalised Daily 2,833 MI hospitalised A 10°C fall in daily mean -Controlled: day of the ov et al., Russia linear model average air temperature between -7°C and - week, seasonal and 2014 1994–2002 temperature. 17°C was associated with an long-term trends. increase in MI of only 2%, while -Adjusted: barometric the same decrement from 25°C to pressure, geomagnetic 15°C was associated with a 21% disturbance, discrete increase in MI. weather events (geomagnetic storms, heat waves and cold spells). -Lagged seven days.
6 Radišauskas Kaunas Linear Mean air 6,753 MI cases registered - In winter period MI rates were Stratified by sex and et al., 2014. population of regressions temperature in MONICA project higher to compare with other age group. MONICA seasons (2 = 18.682, project df = 3, p < 0.001). 1995-2007 - A weak inverse significant correlation between air temperature and morbidity of MI
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(r = -0.05, p = 0.019) (in women and the elderly r = -0.045 and - 0.048, respectively, p < 0.05). - Weak correlation between wind speed and MI morbidity in women (r = -0.042, p = 0.05) and in population of older age (r = - 0.056, p = 0.099). - Weak correlation between atmospheric pressure and MI morbidity was found (r = 0.114 in men and 0.166 in the elderly, respectively, p < 0.01).
7 Tian et al., Hong Kong Distributed Mean 38,292 -Significant nonlinear and -Calculated: 2016 lag nonlinear temperature emergency hospitalisations delayed cold effect but no Attributable risk, 2005–2012 model with apparent hot effect lasting for 3 attributable fraction quasi-Poisson weeks. - Controlled: Influenza, regression. Compared with the identified relative humidity, air optimum temperature at 23.0°C, pollution (NO2, PM10, the cumulative RR during 0 to 21 O3), day of the week, lag days was 2.38 (95% CI, 1.83– holidays. 3.10) for extreme cold (first - Lagged 21 days. percentile) and 1.49 (95% CI,1.24–1.80) for moderate cold temperature (10th percentile). -AMI showed the higher risk of hospitalisation attributed to cold temperature. Cold temperatures were responsible for temperature- related AMI hospitalisations, with attributable fraction of 12.56% for moderate cold and 1.44% for extreme cold while inducing 4,890 and 522 cases, respectively.
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8 Honda et al., Kumamoto two-tailed mean, 642 AMI hospitalisations -Days with ≥ 2 AMI admitted -Controlled: Elderly and 2016 city, Japan paired t-test. maximum, were significantly associated with atmospheric pressure female 2009-2013 chi-square and lower air temperature (mean, and rainfall, humidity, groups. statistic. minimum maximum, and minimum), higher wind speed, and the logistic temperature, intra-day temperature difference, number of sunlight regression intra-day lower humidity, and longer daily hours. analysis. temperature duration of sunlight compared - Lagged three days. difference. with days with fewer than 2 AMI admitted. - Multiple logistic regression analysis showed that minimum temperature two days before onset was associated with the frequent onset of AMI (odds ratio, 0.805; p < 0.05). - Lower minimum temperature on the second day preceding the onset is an independent risk factor for the frequent onset of AMI.
9 Bijelović et Novi Sad, Generalised Ambient 816 AMI Hospitalised of the -The risk for the AMI among the Controlled: season, al., 2017. Serbia linear model temperature Centre adults and the elderly was weekend, relative 2010–2011 with Poisson for the Informatics and significantly higher for 41.8% and humidity and air regression. Biostatics 38.9%, respectively on the days pressure. with lower ambient (≤ 2.7oC), -Stratified by age.
10 Yamaji et Japan Generalised Maximum, 56,863 STEMI of Japanese -Lower mean temperature and -Confounders: day of al., 2016 2011–2012 linear mixed mean, and PCI increase in maximum temperature the week, holiday, models minimum air Registry. from the previous day were administrative districts, temperature. independently associated with the precipitation; mean STEMI occurrence throughout the vapour pressure; and year (OR= 0.925, 95% CI 0.915 - mean local 0.935, per 10oC, p < 0.001; and atmospheric pressure, OR 1.012, 95% CI: 1.009 - 1.015, SO2, NOx, NO, NO2, o per 1 C, p < 0.001, respectively). SPM, and PM2.5. - Decrement in minimum -Stratified: age, groups temperature from L4 days to L3 with or without days before the event date was cardiovascular risk marginally associated with the factors.
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STEMI occurrence, only during the wintertime (OR 0.991, 95% CI 0.982 - 0.999, per 1oC, p=0.03). - The associations with the air pollutant levels were less clear after adjustment for these meteorologic variables. 11 S. Lee et al., South Korea Poisson Mean, 27,388 MI emergency -The threshold temperatures -Adjusted: daily 2014 2006–2010 generalised maximum, visits during heat exposure were for the precipitation; additive minimum, maximum temperature as 25.5– humidity; dew point; model diurnal range 31.5oC and for the mean sea level pressure; and analyses temperature temperature as 27.5–28.5oC. The wind speed, ambient threshold temperatures during 24-h average cold exposure were for the concentrations of minimum temperature as 22.5– PM10, NO2, SO2, O3, 1.5oC. and CO. - RRs of emergency visits above - Lagged seven days. hot temperature thresholds ranged from 1.02 to 1.30 and those below cold temperature thresholds ranged from 1.01 to 1.05. - It was also observed increased RRs ranged from 1.02 to 1.65 of emergency visits when temperatures changed on a single day or on successive days. 12 Kwon et al., South Korea Poisson Mean, 179,099 AMI hospitalised -The Medicaid group, the lowest -Confounders: day of Female, 2015 2004–2012 generalised minimum at Korea National Health SES group, had a significantly the week, annual elderly, addictive and Insurance higher RR of 1.37 (95% CI: 1.07– population, Relative people model maximum Corporation 1.76) for heat and 1.11 (95% CI: humidity, living in temperature, 1.04–1.20) for cold among Precipitation, sea-level urban areas separate for subgroups, while also showing pressure, PM10, NO2, vulnerable summer and distinctly higher risk curves than SO2, CO, O3 to cold, winter National Health Insurance for - Stratified by gender, analysis, both hot and cold weather. age, living area, and individual SES. - Lagged up to 28 days
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13 Mohammadi Tehran, Iran Case- maximum, 15,835 AMI admissions at - Effects of high temperature -Adjusted: Relative Heat effect: et al., 2018. 2013–2016 crossover mean, and Iranian Ministry of appeared immediately on the humidity, air pollutants male and design with a minimum Health and Medical current day and lasted for 3 days, (PM10, SO2, NO2, O3) young distributed lag temperature. Education. whereas cold effects became - Stratified by gender group. non-linear apparent after two days and and age Cold effect model. persisted for about eight days. - Lagged 27 days. for males - Evidence of a harvesting effect only. for high temperature - Cumulative effects by comparing 95th percentile of mean temperature (33.3 °C) with the 75th percentile of mean temperature (28.4 °C) for the short lags ( lag 0–3), the high temperatures were significantly associated with increased risk of AMI admission in males (RR: 1.27, 95%CI: 1.11–1.46) , and people aged ≤ 65 years old (RR: 1.17, 95%CI: 1.01–1.35). -For cold effect, the cumulative effects by comparing 25th percentile of mean temperature (9.8 °C) with the 5th percentile of mean temperature (4.8 °C) was only statistically significant for males at lag 0–27 (RR: 1.32, 95%CI: 1.05–1.66).
14 Wijnbergen Eindhoven, Chi-square Mean 2,983 STEMI hospitalised No influence of temperature on et al., 2012 Netherlands test temperature treated with PCI. the time of the onset of AMI. 2006–2008 RR calculated as ratio to the risk associated with the presumed uniform distribution.
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15 Fernández- Galicia, Spain Chi-squared Mean 4,717 AMI of the Galicia -Correlation between atmospheric García et al., 2002–2009 test temperature Public Foundation pressure and incidence of AMI 2015. for Healthcare was significant (p < 0.05), as well Emergencies. as with the daily relative humidity average (p = 0.05). -No significant correlation between mean temperature and incidence of AMI.
16 Loughnan et Melbourne, Multiplicative Daily 33,165 AMI aged >35 -24 h average temperatures ≤ 9oC -Stratified for sex, age, al., 2010 Australia decomposition maximum and ≥ 30oC, 3-day moving SES, spatial data. 1999–2004 mode and average temperature of ≤ 9oC or - Daily average general linear minimum ≥27oC resulted in increased AMI temperature (9am – model temperatures admissions to hospitals. 9am) and three-day multiple 24-h average - 10.8% increase in admissions on moving average analysis of temperature, days ≥ 30oC. temperature were the variance - AMI increases during hot best indicators of (MANOVA) weather were only identified in increased AMI the most disadvantaged and the admissions. least disadvantaged areas. Districts with higher AMI admissions rates during hot weather also had larger proportions of older residents.
17 Ravljen et Slovania Generalised Average 4,258 STEMI hospital -Confirmed immediate and -Explore effects of al., 2018. 2008–2011 linear model, daily admissions. delayed negative effect of low atmospheric pressure with a Poisson temperature temperature and low relative and humidity. distribution. humidity for all observed lags, as - Lagged 14 days. well as cumulative average effects of low temperature and low relative humidity for all observed time windows. - No delayed, single-day effect for atmospheric pressure was detected.
36 Chapter 2: Literature Review
18 Loughnan et Melbourne, Generalised Daily AMI aged > 35 -Periods of “unseasonable” Stratified by age, sex. al., 2014 Australia linear models average temperature were associated with 1993–2004 maximum increased AMI admissions at and hospitals in Melbourne. minimum - An increase in warmer weather temperatures during the cooler months of spring may result in increased morbidity. 19 Wolf et al., Augsburg, Generalised 24-hour 4,838 non- fatal MIs -For the total MI cases, a 10°C -Adjusted: time trend, 2009 Germany additive mean decrease in five-day average relative humidity, 1995–2004 quasi-Poisson temperature, temperature was associated with a barometric pressure model five-day RR of 1.10 (95% CI, 1.04 - 1.15). season, and calendar average - The effect of temperature on the effects. temperature, occurrence of nonfatal events - Adjusted: influenza 24-hour showed a delayed pattern, epidemics, PNC, PM10, maximum whereas the association with fatal O3 and MI was more immediate. -Lagged five days. minimum temperature, the temperature range, apparent temperature and dewpoint temperature. 20 Wichmann Gothenburg, A time- Daily 28,215 AMI hospitalised -A 3% and 7% decrease in AMI -Adjusted: PM10, NO2, et al., 2013 Sweden stratified case- average hospitalisations was observed for NOx, O3, relative 1985–2010 crossover temperature an IQR increase in the 2-day humidity design, cumulative average of - Effect modification: generalised temperature during the entire year age, sex additive (11oC) and the warm period (6oC) - Lagged 2 days Poisson respectively. models - 1–5% decrease in AMI hospitalizations per 1oC increase in the 2-day cumulative average of temperature.
Chapter 2: Literature Review 37
21 Abrignani et Sicily, Italia Multivariate 24-hour 3,918 AMI -A seasonal variation was found -Controlled for al., 2009 1987–1998 Poisson minimal and with a significant winter peak. multicollinearity and analysis maximal -The IRRs (95% CI) were 0.95 interaction between temperatures (0.92–0.98) (p < 0.001) in males variables: atmospheric as regards minimal temperature pressure, relative and 0.97 (0.94–0.99) (p = 0.017) humidity, bright as regards maximal humidity. The sunshine, rain, wind corresponding values in females direction, and mean were 0.91 (0.86–0.95) (p < 0.001) wind speed. and 0.94 (0.90–0.98) (p = 0.009, - Stratified for sex, respectively). age.
22 Verberkmoes Eindhoven, Multivariate Daily 11,389 AMI Lower temperature was a Adjusted: PM10, O3, et al., 2012 Netherlands linear minimum predictor of a higher incidence of influenza 1998–2010 regression and AMI (p = 0.02). analyses maximum temperatures . 23 Vasconcelos Lisbon Generalised PET 29,220 AMI cases from -The main results revealed a -Adjusted: relative et al., 2013 and Oporto, additive different clinical studies negative effect of cold weather on humidity, barometric Portugal models AMIs in Portugal. pressure, wind speed 2003-2007 - For every degree fall in PET and cloud cover, day of during winter, there was an the week, holidays, increase of up to 2.2% (95% CI pneumonia, influenza, 0.9%- 3.3%) in daily HAs. PM10 - Stratified by age
24 Shiue et al., Germany Two-way PET 63,527 AMI hospitalised -Peaked in winter and early spring 2016 2009–2011 fractional- (including emergency - Admissions had an apparent polynomial admissions) of Germany drop when PETs reached 10 °C. prediction Statistisches plots Bundesamt.
25 Madrigano Worcester– Case- Daily mean 4,765AMI hospitalised -Exposure to cold increased the -Effect modifications: Poor people et al., 2013 USA crossover apparent risk of acute MI, and exposure to socio-demographic vulnerable 1995, 1997, analyses/ temperature heat increased the risk of dying characteristics, medical to heat 1999, 2001 conditional after an acute MI. history, clinical and 2003 logistic - A decrease in an inter quartile complications, and range in apparent temperature physical environment
38 Chapter 2: Literature Review
regression was associated with an increased - Controlled: day of the models risk of acute MI on the same day week, O3, PM2.5, (HR = 1.15, 95% CI = 1.01 – absolute humidity 1.31). - Extreme cold during the 2 days prior was associated with an increased risk of AMI (HR = 1.36, 95%CI: 1.07–1.74). - Extreme heat during the 2 days prior was also associated with an increased risk of mortality (HR=1.44, 95%CI: 1.06–1.96).
26 Morabito et Florence, Italy The Mann– Air-mass 808 MI hospitalised -Significant increases in HAs for Use air-mass-based al., 2006 five winters Whitney U types (based MI were evident 24 h after a day synoptic climatological from 1998– test, variance on characterised by an anti-cyclonic approach, take into 2003 analysis and temperature, continental air mass and six days consideration the the Bonferroni cloud cover, after a day characterised by a simultaneous and test. saturation, cyclonic air mass. complex combination air pressure, - Increased risk of hospitalisation of weather variables. wind speed was found even when specific components) two-day air mass sequences occurred.
Notes: AMI/MI: (acute) myocardial infarction; CI: confidence interval; IQR: interquartile range; IRR: Incident rate ratio; ORs: odds ratio; PCI: percutaneous coronary intervention; PET: physiologically equivalent temperature–generated from air temperature, humidity, wind speed, cloud cover, radiation flux and vapour pressure; PNC: particle number concentration; RRs: relative risks; SES: socio-economic status; and SPM: suspended particulate matter. Grouped by type of temperature exposure.
Chapter 2: Literature Review 39
2.3.2 Mechanism for temperature effect on the morbidity of acute myocardial infarction 2.3.2.1 Mechanism for low temperature effect Potential mechanisms for the cold-induced increased risk for incident coronary events are depicted in Figure 2.3. The stimulation of cold receptors in the skin leads to a rise in catecholamine levels and subsequent vasoconstriction and increased heart rate and blood pressure, which may precipitate myocardial ischaemia and coronary plaque instability (Barnett et al., 2007; Eldwood et al., 1993; Neild, Keatinge, Donaldson, Mattock, & Caunce, 1994; Raven, Niki, Dahms, & Horvath, 1970; Saeki et al., 2014). Moreover, a drop in temperature results in an increase in diuresis, decrease in plasma volume and haemo-concentration, and increase in blood viscosity (Neild et al., 1994). This mechanism is most likely responsible for the observed increase in plasma concentrations of clotting factors and platelet counts, which may promote thrombosis. Finally, inhalation of cold air may elicit pulmonary neurogenic (e.g., orthosympatic) reflexes, which may enhance vulnerability to atherothrombosis and arrhythmia. Beyond direct effects, acute coronary events might also be instigated as a consequence of an exacerbation of pre-existing pulmonary conditions due to cold exposure (Claeys, Rajagopalan, Nawrot, & Brook, 2017).
Figure 2.3 Plausible biological mechanisms linking cold exposure to atherothrombotic events Note: BP: blood pressure, conc: concentration. (Source: Claeys et al., 2017)
40 Chapter 2: Literature Review
2.3.2.2 Mechanism for high temperature effect The adverse effects of high temperatures on AMI admission can be explained by several biological mechanisms. During exposure to hot temperature, blood flow shifts away from the body core to the skin surface to cool the body (Kovats & Hajat, 2008; Wichmann, Andersen, Ketzel, Ellermann, & Loft, 2011) and cardiac output rises significantly due to vasodilation of blood vessels (González‐Alonso, 2012). Prolonged heat exposure can lead to inadequate thermoregulation due to dehydration and salt depletion and may put stress on the cardiovascular and respiratory systems (Basu, 2009; Bouchama & Knochel, 2002). Additionally, high temperature is also associated with elevated plasma viscosity and serum cholesterol levels, which may increase the risk of AMI admission (McGeehin & Mirabelli, 2001; Mohammadi et al., 2018).
2.3.2.3 Mechanism for temperature effects among subgroups Females are more likely to be affected by cold weather compared to males. The potential reasons are that muscle mass, an organ of heat production, is lower in females than males and that females have more difficulty maintaining their body temperature due to additional distribution of blood flow to the uteri and ovaries. In addition, females have a tendency to be more lightly dressed, including wearing skirts, compared to males (Honda et al., 2016).
Older people undergo physiological changes in renal function and electrolyte homeostasis in extremely hot weather (Flynn, McGreevy, & Mulkerrin, 2005) and have a weaker thermoregulation system due to reduced cutaneous thermal sensitivity and diminished skin vasoconstriction with cold stress (Kwon et al., 2015; Smolander, 2002). On the other hand, younger age groups are more vulnerable to heat exposure, physical activity in the summer tends to increase in young subjects compared with older subjects, and the ensuing dehydration and outdoor exposure to hot conditions may be associated with the pathogenesis of AMI onset via activated coagulation (Honda et al., 2016)
Moreover, people with greater poverty have limited resources for coping with a disadvantaged environment. They are therefore at risk of disadvantageous exposures (Madrigano et al., 2013). Living in densely populated urban areas is an important risk factor, with many individuals vulnerable to adverse heat-related health outcomes because of the urban heat island effect (Kwon et al., 2015; McGeehin & Mirabelli, 2001).
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2.4 RELATED STUDIES IN THE VIETNAMESE CONTEXT
There is increasing interest in the effect of environmental elements on health outcomes in Vietnam, especially the temperature effect under growing climate change concerns. However, related research is still limited due to limited data sources. Moreover, these studies have focused on CVDs or other general diseases, such as infectious and respiratory diseases in examining the relationships with temperature variations or extremes.
Phung, Guo, Thai, et al. (2016) examined the short-term effects of temperature on cardiovascular HAs in Ho Chi Minh City, the largest tropical city in Southern Vietnam. They applied Poisson time-series regression models with a distributed lag non-linear model (DLNM) to examine the temperature- cardiovascular HAs (CHA) association while adjusting for seasonal and long-term trends, day of the week, holidays, and humidity. The exposure-response curve of temperature-CHA revealed a J-shape relationship, with a threshold temperature of 29.6oC. The delayed effects of temperature-CHA lasted for a week (0–5 days). There was a sign of an increase in overall risk of CHA by 12.9% (RR, 1.129; 95%CI, 0.972-1.311) during heat wave events, which were defined as temperature ≥ the 99th percentile for ≥2 consecutive days; however, this increased risk was not statistically significant. The modification roles of gender and age were inconsistent and non-significant in this study (Phung, Guo, Thai, et al., 2016).
The relationships between heat waves and hospitalisation for various diseases (infectious, cardiovascular, and respiratory) were also examined in a meta-analysis of 25 cities in Vietnam (Phung et al., 2017). The results showed that the size of province- level effects varied across provinces. The pooled estimates showed that heat waves were not significantly associated with CVD admissions (0.8% increase in CVD admissions at lag 0, 95%CI: -1.6-3.3). The risk of hospitalisation due to heat waves was higher in the north than in the south for CVDs (7.5%, 95%CI: 1.1-14.4 versus - 1.2%, 95%CI: -2.6-2.3) (Phung et al., 2017).
A study in the north of Vietnam was conducted by Pham, Do, Kim, Hac, and Rocklöv (2014) to examine the relationship between daily temperature and daily CVDs HAs among the elderly population. A DLNM was also used to derive specific estimates of the effect of weather parameters on CVD HAs of up to 30 days. The results showed that the average point of minimum CVDs admissions was 26oC. Above and
42 Chapter 2: Literature Review
below this threshold, the cumulative CVDs admission risk tended to increase with both lower and higher temperatures. The cold effect was found to occur 4-15 days following exposure, peaking at a week’s delay. The cumulative effect of cold exposure on CVD admissions was statistically significant, with a relative risk of 1.12 (95% confidence interval: 1.01-1.25) for 1oC decrease below the threshold. The cumulative effect of hot temperature on CVDs admissions was found to be non-significant (Pham, 2014).
2.5 KNOWLEDGE GAPS
To date, there are several knowledge gaps in studies assessing the impacts of temperature on AMI morbidity. Firstly, few studies have focused on this specific issue, particularly in developing countries that have experienced considerable transition in disease patterns. In their systematic review, Bhaskaran et al. (2009b) found that the majority of research on temperature and CVDs was from developed countries, with many of studies from the United States, Asia, and Australia. Studies from the tropical regions are very limited. Within the 26 recent studies focusing on AMI HAs described in Table 2.1, only two were conducted in Hong Kong and Taiwan (Goggins et al., 2013; Tian et al., 2016). No studies have assessed the relationship between ambient temperature and AMI HAs in equatorial regions. For a better understanding of the impacts, this issue should be studied more thoroughly in other regions and countries, particularly in the equatorial zone.
Secondly, researchers have used many different parameters to measure ambient temperature in their studies. The majority of studies used daily mean (average of minimum and maximum temperatures), 24-hour mean, maximum and minimum temperatures as the main exposure indicators (Abrignani et al., 2009; Honda et al., 2016; Loughnan et al., 2010b; Misailidou et al., 2006; Mohammadi et al., 2018). However, some studies used diurnal temperature range (S. Lee et al., 2014; Wolf et al., 2009) or days cumulative mean of temperature (Wichmann et al., 2013). Other composite temperature indices, such as thermo-hydrological index (J. H. Lee et al., 2010), apparent temperature (Madrigano et al., 2013), and human-biometeorological index (PET) (Shuie et al., 2016; Vasconcelos et al., 2013) were also used. Consequently, it is difficult to compare these results or generalise their conclusions.
Moreover, previous studies focusing on AMI and temperature have applied different designs and statistical approaches to address the relationship (i.e., case-
Chapter 2: Literature Review 43
crossover design, time-series analyses, generalised linear model, generalised addictive model) (Bhaskaran et al., 2010; Madrigano et al., 2013; Wolf et al., 2009). Several recent studies examined the lagged effect of environmental stressors on AMI morbidity ( Bhaskaran et al., 2010; Goggins et al., 2013; Wolf et al., 2009). However, a distributed lag non-linear model, which provides a way to simultaneously represent non-linear exposure-response dependencies and delayed effects of environmental factors on health outcomes (Gasparrini, Armstrong, & Kenward, 2010a), has only recently been applied in a limited number of studies regarding this specific health disorder in relation to temperature exposure (Mohammadi et al., 2018; Tian et al., 2016). This modelling approach should be used more often to elucidate the relationships between temperature and AMI morbidity in terms of the exposure- response effect, as well as its prolonged effect.
To the best of my knowledge, no research in Vietnam, a tropical developing country in Asia, has examined the association between temperature and AMI morbidity. Thus, Vietnamese studies have been unable to suggest any specific, evidence-based adaptation measures for protecting the population at risk from these impacts of hot and cold temperatures. It is highly important to develop several specific intervention strategies for the population at risk of AMI as this health condition becomes more common in the country (H. L. Nguyen, Nguyen, et al., 2014). Therefore, this study addresses an urgent need to investigate the linkage between ambient temperature and AMI incidence (HAs) and identify the subgroups vulnerable to these environmental factors. This study also determines the specific temperatures thresholds that substantially increase AMI occurrence (resulting in an increase in the number of hospitalised cases), which will help to provide a more detailed picture of the effects of temperature on AMI occurrence in Vietnam, as well as contribute to the knowledge of this association around the world.
2.6 RESEARCH PROBLEMS
To the best of my knowledge, there is currently very limited research into the weather/temperature impacts on CVDs in Central Vietnam, and in particular, there are no studies on AMI. The few recent relevant studies in Vietnam have primarily been conducted in the north or south of Vietnam, which have large differences in climate and socio-economic status from the central region of the country (Pham, Do, Kim, Hac, & Rocklöv, 2014; Le, Egondi, Le, Do, & Le, 2014; Phung, Guo, Thai, et al.,
44 Chapter 2: Literature Review
2016). Additionally, these studies have only investigated the effects on general mortality and CVDs, not on specific health conditions, such as AMI. This project therefore aims to evaluate the relationship between ambient temperature variation and AMI morbidity in central Vietnam.
Chapter 2: Literature Review 45
Chapter 3: Methodology
This chapter outlines the overall study design and methods used in this thesis. The chapter includes a detailed explanation of the study area, study population, study design, conceptual framework, data collection, data management, and statistical analysis used in this research.
3.1 STUDY DESIGN AND RESEARCH CONCEPTUAL FRAMEWORK
3.1.1 Study design A retrospective study design was used in this study with the use of secondary data from hard-copy medical records of hospitals in the Central Coast of Vietnam from 2008 to 2015. A time-series analytic approach was used along with different modelling techniques to assess the temporal trends and associations between disease risk factors and disease occurrence. A generalised linear model (GLMs) and a distributed lag non-linear model (DLNM) were applied to examine the seasonality of acute myocardial infarction (AMI) hospital admissions (HAs), the pre-hospital admission delay time period, and temperature effects on AMI HAs of the region.
3.1.2 Research conceptual framework A number of factors have been identified that may be related to the onset of AMI (Figure 3.1) and these are usually classified as traditional or non-traditional risk factors (Dahlgren & Whitehead, 1991). Traditional risk factors for AMI include smoking, high blood cholesterol, hypertension, diabetes, obesity, diet, physical activity, alcohol consumption, and psychosocial factors (S. Yusuf et al., 2004); whereas non-traditional risk factors consist of serological biomarkers including fibrinogen, C-reactive protein, homocysteine and elevated Lp(a), which have recently been linked to AMI occurrence (Anderson & Morrow, 2017). Moreover, a number of recent studies have suggested that various environmental factors could be triggers of AMI onset, such as ambient temperature, air pollutants, influenza circulation, etc. (Claeys et al., 2017; Finelli & Chaves, 2011; C. Warren-Gash et al., 2011; Warren-Gash et al., 2009). However, this study primarily focuses on exploring the effects of ambient temperature on AMI morbidity, taking into account the effects of a number of meteorological variables and also influenza circulation. A conceptual framework for this study is presented in Figure 3.2.
46 Chapter 3: Methodology
Figure 3.1 Risk factors of AMI
Note: Adapted from Health Determinant Model–(Source: Dahlgren & Whitehead, 1991)
Figure 3.2 Main conceptual framework for this study
In terms of exploring the delayed effect of ambient temperature or temperature extremes and its association with the pre-hospital delay period, Figure 3.3 presents the time frame from exposure to disadvantageous temperature to presentation at the admitted hospitals (where medical data on AMI cases were collected). The delayed effect of temperature on AMI HAs is related to objectives 3 and 4 of this study, while pre-hospital delay is related to objective 1.
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Figure 3.3 Time frame from temperature exposure to presentation at admitted hospitals of AMI patients
Note: FMC: First medical centre; AMI: acute myocardial infarction; HAs: hospital admissions
3.2 STUDY AREAS AND POPULATIONS
The study area included three provinces: Quang Binh, Thua Thien Hue, and Khanh Hoa. These three provinces were selected based on their locations in two separate regions of the Central Coast region: the North Central Coast region (Quang Binh and Thua Thien Hue), and the South Central Coast region (Khanh Hoa). Support was obtained from provincial health departments to conduct the study at the local hospitals. These provinces were chosen due to the availability and quality of hard-copied medical records and the number of AMI HAs at their local high-level hospitals. The locations of the three studied provinces within the Central Coast region of Vietnam are shown in Figure 3.4.
48 Chapter 3: Methodology
Figure 3.4 Map of the Central Coast of Vietnam and location of the three study provinces
Chapter 3: Methodology 49
3.2.1 Khanh Hoa Khanh Hoa province is a coastal province in South Central of Vietnam, stretching over an area of approximately 5,200 km2. Its geographical coordinates are 108°40’to 109°28’E and 11°43’ to 12°52’ N (Khanh Hoa Provincial Government, 2015). It borders Phu Yen Province in the north, Dak Lak in the north-west, with Lam Dong provinces in the south-west, Ninh Thuan Province in the south, and faces the South China Sea to the east. The province has a 385 km coastline and includes more than 200 offshore islands and archipelagos. Khanh Hoa's population was 1,174,100 people in 2011, representing 6.2% of the total population of the central coast region. The average population density is 225 people/km2 compared to the central region average of 199 people/km2 (General Statistics Office of Vietnam, 2018). The main city of Khanh Hoa is Nha Trang city. The proportion of urban population is 49.8%, and the rural population is 50.2%. Khanh Hoa is the homeland for more than 30 ethnic groups (including Kinh, Raglai, Chinese, Ede, Co-ho, a small group of Tay, Nung, Muong, Thai, Cham, Khmer, and Tho ethnicities), of which the majority are the Kinh, the dominant ethnic group in Vietnam (Khanh Hoa Provincial Government, 2015).
Khanh Hoa province has a tropical savanna climate. In essence, a tropical savanna climate tends to either see less rainfall than a tropical monsoon climate or have more pronounced dry seasons (Khanh Hoa Provincial Government, 2015). However, Khanh Hoa’s climate has several distinct characteristics. Bordered by a pass in the north and a range in the south, the climate of Khanh Hoa is relatively mild compared with the nearby provinces due to the fact that the province receives cool winds from the east ocean. It usually has only two seasons: rainy and dry. It has a short rainy season, from mid-September to mid-December. The rest of the year is the dry season, with an average of up to 2,600 hours/year of sunlight. The annual average temperature of Khanh Hoa is about 26.7 °C. The average relative humidity is 80.5%. The province experiences less storms and typhoons than the rest of the Central Coast. The frequency of storms in Khanh Hoa is low, about 0.82 storms/year (meaning that storms do not appear every year in this province), compared to the average of 3.74 storms/year on the Central Coast of Vietnam (Khanh Hoa Provincial Government, 2015).
3.2.2 Thua Thien Hue Thua Thien Hue is located in the Northern Central Vietnam, with Hue city as the centre, from latitude160N to 16045N and the longitudes 1070E to 1080E (Thua Thien Hue provincial government, 2018). It borders Quang Tri province in the north Da Nang city and Quang Nam province in the south with Hai Van mountain pass, Laos PDR in the west with Truong Son range, and East River in the east. Its capital city is Hue, approximately 600 km from Hanoi and
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1,080 km from Ho Chi Minh city. The province has an area of 5,053.9 km2 that comprises four different zones: a mountainous area, hills, plains, and lagoons separated from the sea by sandbanks, and there are 128 km of beaches (Thua Thien Hue provincial government, 2018). In 2011, the total population of Thua Thien Hue was 1,103,100, accounting for 5.8% of the Central Coast region population, with 546,000 (49.5%) men. The average population density is 219 people/km2 compared to the central region average is 199 people/km2. The proportion of the urban population is 51.7%, the rural population is 48.3% (General Statistics Office of Vietnam, 2018).
The climate in this province is considered to be tropical monsoon, similar to central Vietnam in general (Thua Thien Hue provincial government, 2018). In the plains and the hills, the average annual temperature is 25°C, while in the mountains the temperature is only 21°C. The cool season is from November to March with cold east-north winds. The lowest average monthly temperature is in January at 20°C. In the cool season, temperatures can fall to 12°C in the plains and relative humidity is high, between 85% and 95%, followed by a warmer period from April to September, with average monthly temperatures up to 29°C in July, reaching up to 41°C at times. The annual precipitation in the province is 3,200 mm but there are important variations. Depending on the year, the annual average may be 2,500 to 3,500 mm in the plains and 3,000 to 4,500 mm in the mountains. In some years, the rainfall may be much higher and reach more than 5,000 mm in the mountains. The rainy season is from September to December – about 70% of the precipitation occurs in those months. Rainfall often occurs in short heavy bursts that can cause flooding and erosion, with serious social, economic, and environmental consequences (Thua Thien Hue provincial government, 2018).
3.2.3 Quang Binh Quang Binh is located in the North Central of Vietnam with coordinates 17°05’N to 18°05’ N and 105°37’E to 106°59’ E (Quang Binh Provincial Government, 2018). It borders Ha Tinh Province in the north, with the Ngang mountain pass as the natural frontier, Quang Tri province to the south, Laos PDR to the west, and facing the South China Sea to the east. The provincial topography is characterised by a general slope, higher in the west and lower in the east, with hilly and mountainous areas accounting for 85% of the total area. In the west, the Truong Son Range is the natural border between Quang Binh province and Laos PDR. In the east of the province are lower hills and then several narrow plains and river deltas. The seaside sand dunes belt is a natural dam that protects the land from ocean tides. The capital city of the province is Dong Hoi city. The provincial population is 853,700 (2011), occupying 4.5% of the Central Coast region’s population. The average population density is 106 people/km2,
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compared to the central region average of 199 people/km2. The proportion of urban population is 15.2%, with 84.8% of the population living in rural areas (General Statistics Office of Vietnam, 2018). Twenty-four ethnic groups live here, predominantly Kinh, Van Kieu and Chut. The Kinh ethnic group reside predominantly in urban centres, while the other 23 minority ethnic groups tend to be concentrated in mountainous areas of the province (Quang Binh Provincial Government, 2018)
Quang Binh province has a tropical monsoon climate, divided into two clear seasons: the rainy season and the dry season (Quang Binh Provincial Government, 2018). The rainy season is from September to March each year. The annual average precipitation is 2,000– 2,300mm/year, concentrated in the September, October and December months. The dry season occurs from April to August, with an average temperature 24oC–25oC, although the temperature may reach up to 35oC–36oC. The three months that have the highest temperatures are June, July, and August (Quang Binh Provincial Government, 2018).
Some general information about these provinces is presented in Tables 3.1 and 3.2 below. A comparison of several important social-economic characteristics between 2008 and 2015 (the start and end years of studied period) in these three provinces is also contained in Appendix G. Information on meteorological elements is summarised in Table 3.3.
Table 3.1 Demographic and climatic characteristics of the three studied provinces
Area Population Climate characteristics Provinces (km2) (x1000) Tropical monsoon climate with high Thua Thien Hue 5,033.2 1,140.7 temperature, humidity, radiation, and precipitation.
Khanh Hoa 5,217.7 1,205.3 Tropical savanna climate
Quang Binh 8,065.3 872.9 Tropical monsoon climate. (Sources: Khanh Hoa Provincial Government, 2015; Quang Binh Provincial Government, 2018; Thua Thien Hue provincial government, 2018)
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Table 3.2 Descriptive statistics of socio-economic factors in three studied provinces in 2015 Khanh Hoa Thua Thien Quang Binh Hue Population density 231 227 105 (person/km2) Urban rate (%) 41.9 48.6 19.6 Health staff (doctors, 2274 2154 3155 physicians, nurses, midwifes) Health establishments 166 179 174 In-migration rate (%) 1.6 3.0 3.2 Illiterate ≥ 15 year-olds (%) 5.2 7.4 2.9 Poverty rate (%) 5 4.1 12.5 Monthly average income per 2,904 2,593 2,249 capital (x1,000VND) (Sources: General Statistics Office of Vietnam, 2018)
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Table 3.3 Characteristics of daily meteorological factors by provinces THUE THIEN HUE KHANH HOA QUANG BINH Factors Mean (SD) Range Mean (SD) Range Mean (SD) Range Mean temperature during 24 hours (oC) 25.15 (0.47) 13.2–32.7 27.13 (2.02) 21.1- 31.9 25.07 (4.77) 11.4–35.1 Diurnal temperature (oC) 7.63 (3.19) 0.6–15.7 5.65 (1.44) 1.2–10.9 5.98 (2.25) 1.1–16.4 Minimum temperature (oC) 22.01 (3.22) 10.8–28.9 24.69 (1.96) 17.3–29.6 22.59 (4.36) 10.1–31.9 Maximum temperature (oC) 29.64 (5.48) 14.0–40.2 30.34 (2.47) 22.8–37.6 28.57 (5.62) 12.6–41.0 Diu point temperature (oC) 22.36 (2.94) 11.8–26.3 22.81 (2.20) 13.4–26.3 21.39 (3.91) 8.0–27.9 Relative humidity (%) 86.76 (7.53) 6.0–100.0 78.41 (5.26) 58.0–96.0 82.21 (9.39) 51.0–98.0 Air pressure (mmb) 1009.15 (5.46) 992.8–1024.8 1009.04 (3.46) 993.3–1019.2 1009.70 (5.92) 994.8–1026.3 Wind speed (m/s) 1.12 (0.57) 0.0–7.0 2.73 (1.51) 0.0–10.0 2.11 (1.02) 0.0–8.0 Notes: SD: standard deviation – (Source: National Hydro-meteorological and Environment Network Center, 2016)
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3.3 ETHICAL APPROVAL
This study was regarded as high risk in institutional ethics review due to the lack of participants’ waiver of consent. It was reviewed in four locations. To ensure the all the ethical aspects of the research had been identified and had good management plan, full national ethics application forms were lodged both in Australia (QUT) and Vietnam (Hue UMP). Also, three hospitals involved in the study were informed about the study and asked for their permission prior to the data collections were conducted.
One of the most important ethical issues of the research was the privacy and confidentiality of participants. We were very careful to deal with this issue even though we did not directly contact or work with the people from whom data were gathered. Most of the time, we worked with de-identified information about the participants as secondary data (See the Appendix A – medical data collection sheet). There was a very low risk for participants to be re-identified during a short period of time before the data entry process is completed when a small amount of the health data collection sheets randomly matched with the original health records using the hospital storage ID numbers to check the accuracy of the data entry. However, this process was strictly supervised by the principal researcher to ensure the identity of the participants was not used for any other purpose that could cause harm or discomfort to them. After the data entry process was completed, the list matching the research ID number on the health data collection sheet and the hospital storage ID number of the health record was destroyed. After then, all data were stored with no personal identification details. With all of these safeguards, we satisfied Ethics Committees and hospital leaderships in relation to on participants’ privacy and confidentiality.
This research received approval from the Human Research Ethics Committee of Hue University of Medicine and Pharmacy (Vietnam) in 25th July 2015. In addition, it also received approval from the University Human Research Ethics Committee of QUT (Reference number: 1500000188). These approvals are attached in Appendices C and D.
3.4 DATA COLLECTION PROCESS
The data on HAs were collected from Khanh Hoa General Hospital, Hue Central Hospital, and Vietnam-Cuba Friendships Hospital, the biggest provincial/central hospitals in Khanh Hoa, Thua Thien Hue, and Quang Binh. Influenza-like illness data was obtained from the Statistical year book on infectious disease from 2008 to 2015 (Department of preventive
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medicine-Vietnamese Ministry of Health 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016) from the Vietnamese Ministry of Health, and were available in provincial health departments. Meteorological data was collected from the National Hydrometeorology and Environment Network Centre (National Hydro-meteorological and Environment Network Center, 2016), while air pollutant data were from the Centre for Environmental Monitoring of Vietnam (Centre for Environmental Monitoring (Vietnam Environment Administration), 2016). However, due to the large amount of incompleteness in the recorded air pollutants data set (only three years were available 2013–2015), this data was not used in the analysis of AMI HAs in this study.
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Figure 3.5 Data collection procedure
Note:* = data from three provinces, ** = data for Khanh Hoa and Thua Thien Hue only
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3.4.1 Stage I: Pre-pilot negotiations and consultation with key stakeholders The aims of this stage included formulating the research questions, gathering background information for the research, and establishing the feasibility of the data collection. The main activity during this stage was the implementation of a field trip to Vietnam from January to May 2015 to meet representatives from meteorological and health care services to explore whether the data were available and ask about data quality issues.
As the principal researcher, I travelled to four different areas of Vietnam, including Thua Thien Hue, Khanh Hoa, DakLak, and Hanoi. I met approximately 40 representatives of proposed study sites. They were representatives of three provincial health departments (Thua Thien Hue, Khanh Hoa, DakLak), 13 central/provincial/city/military/private hospitals among three provinces (who provide treatments for most of the AMI cases for the provinces), three regional hydro-meteorological centres (Mid-Central region, South Central region, and Central Highlands region), the National Hydro Meteorological and Environment Network Centre and the Centre for Environmental Monitoring (Vietnam Environment Administration), the Institute of Community Health Research (HUMP), cardiological specialists from Vietnam National Heart Institute (Bach Main Hospital, Hanoi), and staff from the Centre for Climate Change Study in central Vietnam. In addition, I met with a number of Vietnamese cardiologists, environmental health experts, and staff who were in charge of the storage of medical records and electronic medical information management of hospitals. I also worked with representatives of the provincial health departments and staff of several hospitals in Danang city and Quang Binh Province to explore the feasibility of conducting the research in their provinces. Lastly, three hospitals in Khanh Hoa, Thua Thien Hue and Quang Binh were selected to implement the study. A picture of the author and others auditing storage facilities of a visited hospital for hard copy medical records is shown in appendix E.
During the meetings with the representatives from the hospitals and provincial health departments, the feasibility of implementing the research in their hospitals was discussed, seeking general information about the patterns of AMI HAs in the hospitals and provinces (estimated numbers per year, transferral percentage, etc.), and the procedures for medical data management in the hospitals. Their willingness to participate in and support the research was also ascertained, in addition to discussing the requirements for being involved in the research and local background information for the research. During meetings with the cardiology and environmental health experts, the research significance, design, potential statistical approaches, and so on were discussed. The availability and accessibility of meteorological data and air
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pollution data was discussed with staff from local and national meteorological and environmental monitoring centres. There was no primary data collection and no secondary data analysis undertaken during the field trip.
In general, the feedback from key stakeholders suggested that the research was feasible, and the consultations were useful in aiding the selection of the provinces where the data were available, while avoiding sites where there was insufficient data. On the basis of these discussions, Khanh Hoa General Hospital in Khanh Hoa province, Hue Central Hospital in Thua Thien Hue, and Vietnam-Cuba Friendships Hospital in Quang Binh were recruited for the research. Furthermore, an initial estimate of the number of AMI HAs at those hospitals was calculated, and this information informed an initial budget required for the study, including the salaries of research assistants, as well as the development of a medical data collection tool. Following this, I was able to successfully secure a small funding grant of $5,000 from the Institute of Community Health Research (Hue University of Medicine and Pharmacy) to support the research.
Overall, the health care system in Vietnam comprises a mixed public-private provider system in which the public providers still occupy the dominant part. There are four levels of public medical centres: central and regional hospitals, provincial hospitals, district and city hospitals, and commune health centres. Additionally, there is a network of village health workers managed by the commune health centres, serving in the vast remote or mountainous areas (Ministry of Health Vietnam and the Health Partnership Group, 2008; United States Agency for International Development, 2009). Low-level medical centres provide first aid care for general and less severe health conditions, as well as managing national health programmes (i.e., vaccination, hypertension control programmes); whereas high level hospitals (such as provincial or central level hospitals) provide specialised treatments and complex medical procedures. Patients could be “referred” to other medical centres or hospitals if those medical centres do not have adequate resources to control the clinical condition of the patient, seeking support from a better-equipped facility or to reduce the overload at the same time and reduce the costs for the patients (Ministry of Health Vietnam, 2014). In regards to the AMI patients in this study, patients were normally referred to provincial or central hospitals for reperfusion treatment. This referral process might delay patients’ sufficient time for this treatment if they are initially hospitalised at low-level medical centres located far from provincial/central hospitals, or fail on AMI diagnosis at a lower level treatment centre. Among the recruited hospitals, the Khanh Hoa General Hospital is a provincial hospital; while Hue Central Hospital
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and Vietnam-Cuba Friendships Hospital are central hospitals. They are all the largest hospitals in the Khanh Hoa, Thua Thien Hue, and Quang Binh provinces, respectively, estimated to cover treatment for more than 80% of residential AMI patients in those areas.
3.4.2 Stage II: Pilot Study The pilot study was conducted from the middle of February 2016 to the middle of March 2016. This took place following the successful completion of the confirmation seminar for PhD candidature, and obtaining ethical approval from Human Research Ethics Committees of HUMP and QUT. Expected outcomes of this pilot study included obtaining data detailing the percentage of misdiagnosed AMI, assessment of the availability and quality of information on the hard-copied medical records, estimation of the time required for finding the hard-copy medical records according to storage ID, and estimation of the time required to copy the necessary information from the individual medical record to the structured medical data collection sheet. During the pilot phase, the feasibility of the structured medical data collection sheet was also tested and the study scope was modified as needed. At the end of this stage, a medical data collection sheet was developed and the suitability of this sheet was assessed. Following this, a detailed plan for the data collection phase was then prepared.
The structured medical data collection sheet (Appendix A) was initially created by the research team and was later modified after consultation with experts from the formative field trip. This instrument was originally developed in English and then translated into Vietnamese by the principal researcher. Following this, the Vietnamese version (Appendix B) was translated into English by another independent professional. The English was checked again with the original version to ensure equivalence with the original. Further, the wording of the sheet was revised in a small focus group discussion of research assistants and monitors who were in charge of the medical data collection in hospitals. The sheet was then modified based on the comments received during the discussion. All of these steps ensured the equivalence between the Vietnamese and English versions, as well as consistent understanding of the use of this tool among the research assistants and monitors.
The pilot study examined 52 AMI hard-copy medical records from Hue Central Hospital from 2008–2014. These 52 storage ID numbers of AMI medical records (ICD-10: I21, ICD-9: 410) were randomly selected from the electronic medical information management system of the hospital. A list of these 52 storage IDs was used to find the corresponding hard-copy medical records stored in different storage areas of the hospital. Following this, a qualified
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doctor helped to check whether the discharge diagnosis offered for the selected cases were correct, based on information contained in the medical records and standard diagnosis criteria. Six out of these 52 medical records (11.5%) did not meet the criteria for AMI diagnosis. The required information was extracted from each of the eligible medical records to populate a structured data collection sheet (see Appendix B). The full procedure for finding an AMI medical record to completing the structured medical data collection sheet was facilitated using a checklist. Information obtained from the completed medical data collection sheets and checklists was analysed to make a detailed plan (timeline, personnel, and budgets) for the main data collection.
3.4.3 Stage III: Main data collection 3.4.3.1 Medical data The main study, including collecting AMI HAs data, was conducted from April to July 2016. It consisted of the collection of AMI HAs from three hospitals: Khanh Hoa General Hospital in Khanh Hoa provinces (1,342 eligible cases), Hue Central Hospital in Thua Thien Hue (1,274 eligible cases), and Vietnam-Cuba Friendships Hospital in Quang Binh (712 eligible cases). The data collection included AMI HAs from 1st January 2008 to 31st December 2015.
The processes to access and collect AMI data were similar to the processes used in the pilot study. All ID storage numbers for AMI medical records from the electronic medical information management system of the hospitals from 2008–2015 (ICD-10: I21, ICD-9: 410) were collected, excluding a number of cases that were not residents of the studied areas and were less than 18 years old. The list of storage IDs was used to find the corresponding hard- copied medical records. Following this, qualified doctors helped to check the accuracy of the diagnosis of AMI in the medical records. The medical records that satisfied both inclusion criteria and exclusion criteria (details presented in Section 3.5) were used to extract the required information into a hard-copy structured health data collection sheet (see Appendix B).
Trained research assistants accessed the selected medical records, transferring the required information into the health data collection sheet. No identifying information was collected during this process. They were also responsible for entering data from the health data collection sheet into an electronic database created following the structure of the health data collection sheet and EpiData software ver.3.1, which allowed for initial control of the quality of data entered. In the meantime, groups of research monitors (the staff of the Institute of
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Community Health Research, Vietnam) provided supervision for the health data collection, transfer, and entry. The staff observed the health data collection process and accessed 20% of the medical records and database to check and correct inaccurate information during the health data transferral and entry. Following this process, the principal researcher continued to clean the data by cross-checking consensus between the variables and comparing information on the hard-copy health data collection sheet. Several pictures of the main activities for medical data collection are in Appendix E.
At the end of the data collection process, 3,659 hard-copied medical records were located (from 3,777 records online and through record books), leaving 118 records (3.1%) missing. Moreover, during the process of rechecking the AMI diagnosis and exclusion criteria for every single record, another 331 (8.8%) medical records that did not meet the selection criteria were excluded, leaving 3,328 records for the main data analysis. Of the 3,328 hard-copy medical records collected, 72 records (2.2%) did not provide information about the anticipated time from the AMI onset to hospitalisation in any part of the records; therefore only 3,256 AMI cases were included in the analysis of pre-hospital delay time for AMI. An important point worth mentioning is that the proportion of missing hard-copy medical records and proportion of medical records removed from the study based on the selection criteria (see the inclusion and exclusion criteria in the AMI selection criteria and collection information in Sections 3.5.1.1 and 3.5.1.2) were 5.8%, 8.2%, and 26.1% across these three hospitals. Figure 3.6 shows the flowchart of data collection process in the hospitals.
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Figure 3.6 Flowchart of data collection process in studied hospitals
3.4.3.2 Meteorological data Main meteorological data collected includes:
Daily mean temperature (oC) (the average of 24-hour temperatures from midnight to midnight); Daily average temperature (oC) (the average of daily maximum and minimum temperatures); Daily minimum and maximum temperature (oC); Dew point temperature (oC). Other daily data related to other meteorological elements: relative humidity (%), air pressure (mmb), and wind velocity (m/s) were also collected. These data were recorded in three main meteorological monitoring stations, namely Dong Hoi station (of Quang Binh province), Hue station (of Thua Thien Hue province), and Nha Trang station (of Khanh Hoa province). The longest distances were estimated to be around 120 km, 60 km, and 70 km from the far end of each province to the stations for Quang Binh, Thua Thien Hue, and Khanh Hoa respectively. The meteorological data were stored in the National Hydro-meteorological and Environment Network Center in Hanoi, Vietnam. The data were collected at this centre for the eight-year period from the 1st of January, 2008 to the 31st of December, 2015.
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3.4.3.3 Air pollutant data A range of air pollutant data was also collected, including daily mean particulate matter 3 3 less than 10µm in aerodynamic (PM10) (µg/m ), daily mean fine particulate (PM2.5) (µg/m ), 3 daily mean nitrogen dioxide (NO2) (μg/m ), and daily mean ozone (O3) (ppb). Data from 20/02/2012 to 31/12/2015 for two automatic monitoring stations in Thua Thien Hue and Khanh Hoa provinces were obtained from the Centre for Environmental Monitoring (Vietnam Environment Administration) (Centre for Environmental Monitoring (Vietnam Environment Administration), 2016).
Air pollution was a potential effect modifier/independent variable of the studied associations between ambient temperature and HAs for AMI (Bhaskaran et al., 2009a; Nuvolone et al., 2011). However, due to the fact that there was no air pollutant data available for Quang Binh and a large number of missing data in the obtained dataset, this data was not able to be used for any of the analysis
3.4.4 Stage IV: Expert consultation on long - term trends of hospital admissions due to acute myocardial infarction and influenza data collection The main purpose of this stage was to seek advice from experts from the hospitals and related provincial health departments about any unusual trends related to AMI HAs in 2013 and collect local influenza data to add into the analysis later. The main activity during this stage consisted of a field trip to Vietnam from June to early July 2017 to meet two to three representatives from the cardiovascular departments of each hospital and staff from the provincial health departments to obtain the targeted information and data.
After consultation with doctors at the cardiovascular departments of the studied hospitals three main reasons were provided for the unusual trends for AMI HAs in 2013 in the different provinces. The first and the second reasons suggested were the arrival of the third universal definition of AMI in late 2012 and using highly sensitive Troponin, a biomarker for AMI detection, in 2013. Both factors were likely to support the high increase of AMI incidence in this year. Further, the decrease of AMI HAs in Quang Binh in 2013 could be explained by the fact that there was no cardiovascular intervention unit available in Vietnam-Cuba Friendships Hospital at that time. In 2013, the other two cardiovascular intervention departments in Hue Central Hospital (Thua Thien Hue province) and Khanh Hoa General Hospital (Khanh Hoa province) had been well-developed for about a decade (in Thua Thien Hue) and half a decade (in Khanh Hoa). They were well-known as being good departments for cardiovascular intervention, applying a wide range of sufficient high-technology for cardiovascular
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interventions such as percutaneous coronary intervention or coronary artery bypass grafting. Therefore, with an effective scheme for attracting AMI patients from other nearby provincial hospitals with cardiovascular intervention departments during this time, a number of AMI cases in Quang Binh would pass by the Vietnamese-Cuba Friendships Hospital (without an official referral processes) and go to Hue Central Hospital (included in the study) or the Vietnam Heart Institute in Hanoi (not covered in study due to the limits of time and resources), and this could have led to a drop in the number of AMI HAs at the hospital.
3.4.4.1 Influenza-like illness data During this field trip, data were also collected on the number of influenza-like illness cases from each of the provinces for the eight-year period from 2008 to 2015. These data were obtained from the Statistical Year Books of Infectious Diseases and stored at provincial health departments (Department of preventive medicine-Vietnamese Ministry of Health 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016). These data (monthly counts) were converted into a daily scale (by dividing the monthly count by the number of days in that month) for use in the short- term exposure models (see Chapters 6 and 7).
3.4.5 Role of the PhD researcher and research assistants for data collection process The PhD student, as the principal researcher of this study, was responsible for conceptualisation and every stage of the research. She was in charge of providing training workshops on data collection, entry and monitoring processes for research assistants. She also supervised general medical data collections and entry in all three hospitals. She directly went to Hanoi and obtained obtaining meteorological data, air pollutant data and influenza-like illness data.
In each hospital, six trained staff helped to transfer medical data from the hard-copy medical records in to medical data collection sheet. They were also in charge of entering all data into electronic data files (Epidata files). Moreover, a qualified doctor was recruited to recheck the accuracy of the diagnosis of AMI in the medical records, excluding those did not match the criteria for AMI (see Section 3.5.1.1 and 3.5.1.2).
3.4.6 Reliability and rigour of the data and their management The data collected in this study was carefully selected and gathered using procedures to enhance reliability. The researcher liaised regularly with field staff to monitor their conduct and ensure procedures are being followed appropriately. By cross-checking AMI hospitalisation information from hard-copy health records with the confirmation on discharge
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diagnosis by qualified doctors, our data were richer and possibly superior than those in similar studies that used only electronic records.
Meteorological and air pollution data were directly collected by the National Hydro- meteorology and Environment Network Centre and the Centre for Environmental Monitoring (Vietnam Environment Administration). AMI hospital admissions and related information were obtained through transferring information from health records (six collectors for each hospital), then entering into electronic files. This process was monitored by four research assistants from the Institute of Community Health Research (Hue, Vietnam). All the data collectors and monitors attended 1-2 days training on data collection and entering processes conducted by the candidate (the principal researcher). I also provided general supervision for the whole process.
For the monitoring process, the research assistants (monitors) undertook unannounced visits to the hospitals to undertake research activities such as quality assurance, study validation and project staff support (once or twice a week). Moreover, they also randomly matched 10% of the completed health data collection sheets with information shown in the corresponding health records to check the accuracy of the data transferral and to correct wrong information.
3.5 ACUTE MYOCARDIAL INFARCTION SELECTION CRITERIA AND COLLECTION INFORMATION
3.5.1 Acute myocardial infarction selection data AMI data were extracted from hard copies of all AMI medical records dated between January 1st 2008 to 31st December 2015 from the following three provincial/central hospitals:
Provincial General Hospital of Khanh Hoa (Khanh Hoa province)
Hue Central Hospital (Thua Thien Hue province)
Vietnam–Cuba Friendship Hospital (Quang Binh province)
AMI cases were classified according to the International Classification of Disease (ICD) version 10 (Vietnamese Ministry of Health 1998) and main discharge diagnosis as “acute myocardial infarction” (in Vietnamese: “Nhoi mau co tim cap”) and resident details from the three studied provinces were used to extract the AMI cases. Hospitalisation for an AMI was defined as a discharge (dead or alive) with a principal diagnosis of ICD 10–I21- or “acute myocardial infarction” (in Vietnamese: “Nhoi mau co tim cap”).
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3.5.1.1 Inclusion criteria: Adapted from the third universal definition of myocardial infarction (Thygesen et al., 2012), either one of the following criteria satisfied the diagnosis for an acute, evolving or recent myocardial infarction:
Figure 3.7 Criteria for AMI diagnosis (Source: The third universal definition of myocardial infarction - Thygesen et al., 2012)
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3.5.1.2 Exclusion criteria A number of AMI HAs was also excluded from the three studied hospitals. These were:
AMI patients who did not live in Khanh Hoa, Thua Thien Hue, or Quang Binh.
AMI patients under 18 years old.
AMI hospitalisations that occurred within 28 days of a previous AMI hospitalisation – as readmissions following discharge for AMI are quite high (subsequent myocardial infarction) (Wichmann et al., 2013).
In addition, in cases of AMI hospitalisations for the same person occurring at more than one hospital on nearby days (i.e., transfers), medical records of the hospital at the highest level were retained in the study.
AMI patients hospitalised with a percutaneous coronary intervention arrangement.
3.5.2 Collection information The following medical-related variables were identified and collected: Main outcome – daily AMI HAs (count numbers). General information about AMI cases: o age; o gender; o suburb; o occupation; o having health insurance. Hospital admissions details: o period of time from when the first signs/symptoms occurred to arrival at the first health facility. (This information was then used as the estimate of pre-hospital delay time for objective 1’s analysis – See Section 3.6.2). o details about HAs at the first medical centre; o details about HAs at current hospital; o details about AMI types/conditions (STEMI or non-STEMI, Killip class).
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The Killip classification is a valuable prognostic stratification for patients with AMI, taking into account physical examination and the development of heart failure in order to predict and stratify their risk of mortality. Individuals with a low Killip class have a better prognosis after their myocardial infarction than individuals with a higher Killip class (H. Cheng & Yen, 2010). Records of this classification for AMI patients were collected from the admission hospitals.
cardio-vascular condition history;
co-morbidity;
health status at the discharge time.
A summary of the data set and sources used in this study is provided in Table 3.4.
Table 3.4 Summary of data set and data sources used in this study Data Source Type of data AMI medical data Provincial General Hospital of Daily AMI HAs (2008-2015) Khanh Hoa Age Hue Central Hospital Gender Vietnam–Cuba Friendship Province Hospital Occupation Health insurance status HAs details Details of AMI types/conditions Cardio-vascular condition history Co-morbidity Treatment Meteorological data National Hydrometeorology and Daily mean temperature (2008-2015) Environment Network Centre Daily minimum temperature Daily maximum temperature Daily mean dew point temperature Daily mean relative humidity Daily mean air pressure Daily mean of wind velocity Influenza like illness Three provincial health Monthly count for influenza like (2008-2015) departments illness per province
Air pollutant data Centre for Environmental Daily mean of PM10
(2012-2015) Monitoring (Vietnam Daily mean of PM2.5
For Khanh Hoa and Environment Administration) Daily mean of SO2
Thua Thien Hue (A large amount of missing data) Daily mean of NO2 provinces only Daily mean of CO
Daily mean of O3
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3.6 DATA ANALYSIS
Various statistical techniques were applied to the medical and environmental data sets, according to different time-scales: seasonal, monthly, and daily data. Each are described below.
3.6.1 Descriptive analysis of acute myocardial infarction cases Sample size: 3,328 AMI cases in three hospitals
Data period: eight years from 1st January 2008 to 31st December 2015.
Methods applied: Numbers, percentages with tables, and graphs were used for the description of the AMI population in the study and meteorological and air pollutant data (Kirkwood, Sterne, & Kirkwood, 2003; Miller & Miller, 2010).
Software used: Statistical Package for Social Sciences software (SPSS 23 for Windows, SPSS Inc., Chicago, IL, USA).
The results for this analysis are presented in Chapter 4.
3.6.2 Pre-hospital delay time for acute myocardial infarction Sample size: 3,356 AMI cases
Data period: eight years from 1st January 2008 to 31st December 2015
Logistic regression was used to examine the factors associated with delays in hospital arrival time (Kirkwood et al., 2003; Miller & Miller, 2010).
Information on pre-hospital delay time (e.g., < 6h, 6–12h, 13–24h and > 24h) was converted into a binary variable using a 12 hour threshold as the criterion for separation. This threshold was the preferable time for AMI treatment based on the recommendation of Vietnamese Ministry of Health (Vietnamese Ministry of Health 2013).
A bivariate analysis was initially performed to investigate the relationship between the time that had elapsed since the onset of symptoms until the patient’s arrival at the hospital and each variable separately. Chi-square test and Fisher’s exact test were used to identify differences between the groups for the total sample, and the analysis was undertaken for the total sample size. Variables that were significantly associated (p < 0.05) in the bi-variate analysis were entered into a backward stepwise multivariate logistic regression analysis. The criteria for entry and removal of the
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variables were based on the likelihood ratio test, with entry and removal limits set at p< 0.10 and p ≥ 0.10, respectively. Adjusted odds ratios (OR) were estimated with 95% confidence intervals (CI) for the risk factors included in the model. Uni-variable regressions were also performed to obtain the crude OR with 95% CI for the significantly associated variables with the delay time in bi-variate analysis. All tests of statistical significance were two-tailed, and p values of less than 0.05 were considered significant.
Software used: Statistical Package for Social Sciences software (SPSS 23 for Windows, SPSS Inc., Chicago, IL, USA).
Details about and the results of the analysis are presented in Chapter 4.
For later research objectives (i.e., trends and seasonality of AMI HAs, ambient or extreme temperature effects on AMI HAs), the original dataset (based on AMI cases) was converted into time-series datasets by aggregating data (summing the number of AMI cases) by various time scales (i.e., daily, monthly, seasonally, and yearly) and subgroups (i.e., groups of males; females; < 65 year old; ≥ 65 year old; those with CVDs in their history; those with no history of CVDs, STEMI, or non- STEMI; those with at least one comorbidity; those with no comorbidity; those initially hospitalised in a provincial or central level hospital; and those initially hospitalised in lower level hospitals).
3.6.3 Long-term trends and seasonal analysis For long-term trend analysis, AMI HAs were aggregated by year, plotting the trends and conducting the regression of yearly AMI HAs.
For seasonality analysis, seasonal and monthly data were used by aggregating AMI HAs by seasons and months, using a generalised linear model (GLMs) for seasonal data and monthly data of AMI HAs for three separate provinces with the basic formula, as follows:
Yt ~ negative binomial (μt)
Log (μt) = α + β1*winter + β2*spring + β3*fall + β4*year + offset (pop)
in which the outcome (Yt) is AMI HAs counts per season per year. Long-term trends were controlled for by controlling for the year as the category variable in the model. For the purpose of the analysis, seasons were defined based on the
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meteorological seasons. Studied sites were in the northern hemisphere; therefore, winter: 1st December–28th or 29th February, spring: 1st March–31st May, summer: 1st June–31st August, autumn: 1st September–30th November. The length of each season was adjusted to 91 days to take into account the different lengths of each season (D’Souza et al., 2007; Ornato, Peberdy, Chandra, & Bush, 1996; Sheth et al., 1999; J. Yang et al., 2017). The analysis was repeated on different subgroups of gender and age groups (i.e., < 65 year old and ≥ 65 years old) to compare the seasonality of AMI HAs among various groups.
Yt ~ negative binomial (μt)
Log (μt) = α + β1*year + β2*Jan + β3*Feb + β4*Mar + β5*Apr + β6*May + β7*Jun
+ β8*Jul + β9*Aug + β10*Sep + β11*Oct + β12*Nov + β13*Dec + offset (pop)
in which the outcome (Yt) is the AMI HAs counts per month per year. The length of each month was adjusted to 30 days to take into account the different lengths of each month. The number of AMI cases were scaled in months with 28, 29, 31 days to 30 of the AMI HAs.
Details and the results of the analysis are presented in Chapter 5.
For the objectives to examine the ambient temperature or extreme temperature effects on AMI HAs, a distributed lag non-linear model (DLNM) was applied for the analysis. This modelling allows for the description of simultaneous non-linear and delayed effects between predictors and outcomes, a dependency defined as exposure- lag-response association. This means the modelling was not only used to assess the association between the number of AMI HAs on a given day to the exposure levels on that day, but also to explore the association between outcomes on a given day and exposure on previous days. When used effectively, this type of modelling and the R package (Bhaskaran, Gasparrini, Hajat, Smeeth, & Armstrong, 2013; Gasparrini et al., 2010a; Gasparrini & Armstrong 2015) creates time-shifted copies of the exposure variable to be included in the model
3.6.4 Short–term effects of ambient temperature on hospital admissions due to acute myocardial infarction Sample size: 3,328 AMI cases as daily HAs counts
Data period: eight years from 1st January 2008 to 31st December 2015 (2,922 days)
72 Chapter 3: Methodology
Methods applied Time-series regression methods were applied to examine the association between temperature and AMI admissions using GLMs and DLNM (Gasparrini et al., 2010a). As there was some evidence of over-dispersion in the exploratory models, HAs for AMI were assumed to follow a negative binomial distribution. Alternative testing was also undertaken using a quasi-Poisson model (J. Cheng et al., 2016; J. Yang, Ou, Ding, Zhou, & Chen, 2012).
In the exploratory analysis, the daily mean temperature was selected as the temperature measure for further analysis. This selection was done for two main reasons. First, other indicators (maximum or minimum temperature, as well as dew point temperature) showed a high correlation with each other in the dataset (Chapter 4). Mean temperature was also found to be a better predictor than other temperature indicators across different models in previous studies (M. E. Loughnan et al., 2010b; Misailidou et al., 2006), and it has also commonly been used in similar research in other regions (Radišauskas et al., 2014; Ravljen et al., 2018; Tian et al., 2016).
Variables in the GLMs model included AMI HAs, long-term trends, seasonality, weekend and holiday, influenza-like illness incidence, and other meteorological factors. Seasonality and long-term trends were adjusted by time (ordering number from 1 to 2,922 as the total number of the studied period) and months as a factor, weekend and holiday (as dummy variables – holidays and weekends were coded 1), and a spline function for daily mean relative humidity, air pressure and wind velocity, influenza- like illness with three degrees of freedom. A two-stage model was used to separately examine the influence of temperature after adjusting for the effect of other influences (seasonality, trend, relative humidity, etc.), where the residuals from the first stage of the model were used in the second stage. In the first stage model:
Yt ~ negative binomial (μt)
Log (μt) = α + Time + month+ ns(RHt, 3df) + ns(APt, 3df) + ns(WSt, 3df) + ns
(Inft, 3df) + Wendt + Holidayt+ offset(log(pop))+ Et (1)
where t refers to the day of the observation; Yt is the observed daily AMI HAs on day t; RHt is the mean relative humidity on day t; APt is the mean air pressure on day t; WSt is the mean wind speed on day t; Inft is the estimated mean influenza-like illness count on day t; ns(.) denotes the natural cubic spline functions; Wend is the
Chapter 3: Methodology 73
weekend; Holiday is holiday; Et is the residual of the model and log(pop) is the log scaled population which is used in the model (1) as an offset.
In the second model (Equation 2), the residuals of the previous model (Equation 1) were used to estimate the effect of daily mean temperature on AMI HAs,
fit2 = ŷ
Log (Et) = α + ns (Tt-l, 4df) + offset (log (fit2)) + εt (2)
Where Tt-l is a matrix created by DLNM for daily mean temperature on day t with l lag days (21 days in this study) and ŷ represents the fitted values from the first model (Equation 1).
In order to capture the non-linear and delayed effects of temperature, a DLNM with 4 degrees of freedom natural spline function for temperature, and 4 degrees of freedom natural cubic spline for lag up to 21 days was used, which had previously been used and found to be suitable (Guo, Barnett, Pan, Yu, & Tong, 2011; Guo et al., 2013; Guo et al., 2014; Phung, Guo, Thai, et al., 2016) (Equation 2). In order to determine the reference temperature for the DLNM, the threshold points where the rate of AMI HA was lowest in the multi-variable model were chosen for each region. The temperature corresponding to the minimum estimated relative risk within 5th to 95th percentile of temperature range was selected. The selected threshold has been described in other studies (Tian et al., 2016). Finally, the threshold temperatures were determined to be 23.4oC and 21.2oC in the South Central Coast region and the North Central Coast region, respectively.
To quantify the effects of temperature on AMI admission, the cumulative relative risks (RRs) were calculated at a specific temperature (5th, 10th, 90th, and 95th percentiles of temperature distribution) compared to the reference temperatures (selected threshold temperatures). The cumulative effect, or overall effect, was computed by summing all of the contributions at different lags (Gasparrini et al., 2010a). Other reference temperatures were also used in a sensitivity analysis. The delayed effect was examined for specific days (from lag 0 to lag 21), and the relative risks calculated for each of these days. Moreover, a cumulative delayed effect was estimated by calculating the cumulative relative risk of AMI HAs from the day of exposure to the selected lag day (i.e., RR for the first two days–lag 0-1; RR for the first seven days–lag 0-6). A subgroup analyses was also performed to evaluate whether
74 Chapter 3: Methodology
there was any effect modification by gender (male and female), age (18–64 and 65+years), and health status (STEMI or Non-STEMI, having pre-cardiovascular disease or not, and having a comorbidity or not).
The data analyses were carried out using “MASS” and “dlnm v. 2.1.3” packages of R software (R Foundation for Statistical Computing, version 3.2.5, 2016 (Gasparrini & Armstrong 2015; Venables & Ripley, 2002).
Further details and the results of the analysis are presented in Chapter 6.
3.6.5 Added effects of extreme temperatures on hospital admissions due to acute myocardial infarction Sample size: 3,328 AMI cases as daily HAs counts
Data period: eight years from 1st January 2008 to 31st December 2015 (2922 days)
Methods applied
Defining heat waves and cold spells
A recent multi-city time-series study showed that the impact of a heat wave on mortality started to increase around the 95th percentile of daily mean temperature and rose alarmingly at the 99th percentile (Tong et al., 2015); hence, in this study, different temperature metrics (intensity and duration) were used to define local heat waves. A range of percentiles of daily mean temperature were used (95th, 96th, 97th, 98th, and 99th) to define a temperature threshold, and an extreme heat referred to daily mean temperature above these thresholds. Furthermore, a heat wave was defined using the combination of intensity (95th, 96th, 97th, 98th, and 99th percentile of daily mean temperature of the entire data series) and duration (≥ 2 consecutive days, ≥ 3 consecutive days, and ≥ 4 consecutive days) (J. Cheng et al., 2016). For example, a heat wave can be defined as a minimum of two consecutive days with a daily mean temperature above the 95th percentile. The same approach was also used to define a cold spell, except that the 5th, 4th, 3rd, 2nd, and 1st percentiles of daily mean temperature were used. Tables 3.5 and 3.6 summarise the number of heat waves and cold spells in the South Central Coast region and the North Central Coast region based on the various definitions. Details of AMI HAs during different definitions of heat waves and cold spells among various subgroups are summarised in Appendix M.
Chapter 3: Methodology 75
Table 3.5 Summary of heat wave events during the period 2008-2015 using different heat wave definitions (HWDs) in South Central Coast region, Vietnam HWDs Cut-off percentile Duration Number of Total AMI of temperature heat wave HAs on days (days) events with heat (oC) wave 1 95th (29.9oC) ≥ 2 days 122 53 2 95th (29.9oC) ≥ 3 days 80 36 3 95th (29.9oC) ≥ 4 days 77 35 4 96th (30.0oC) ≥ 2 days 102 48 5 96th (30.0oC) ≥ 3 days 70 32 6 96th (30.0oC) ≥ 4 days 64 31 7 97th (30.2oC) ≥ 2 days 78 40 8 97th (30.2oC) ≥ 3 days 58 28 9 97th (30.2oC) ≥ 4 days 46 27 10 98th (30.4oC) ≥ 2 days 41 26 11 98th (30.4oC) ≥ 3 days 33 24 12 98th (30.4oC) ≥ 4 days 24 13 13 99th (30.7oC) ≥ 2 days 20 15 14 99th (30.7oC) ≥ 3 days 12 12 15 99th (30.7oC) ≥ 4 days 9 9
76 Chapter 3: Methodology
Table 3.6 Summary of cold spell events during the period 2008–2015 using different cold spell definitions (CSDs) in the North Central Coast region, Vietnam CSDs Cut-off percentile Duration Number of Total AMI of temperature (days) cold spell HAs on days (oC) events with cold spell 1 5th (16.8oC) ≥ 2 days 135 102 2 5th (16.8oC) ≥ 3 days 109 90 3 5th (16.8oC) ≥ 4 days 82 67 4 4th (16.4oC) ≥ 2 days 98 77 5 4th (16.4oC) ≥ 3 days 78 67 6 4th (16.4oC) ≥ 4 days 60 54 7 3rd (16.0oC) ≥ 2 days 73 69 8 3rd (16.0oC) ≥ 3 days 61 59 9 3rd (16.0oC) ≥ 4 days 52 49 10 2nd (15.5oC) ≥ 2 days 43 44 11 2nd (15.5oC) ≥ 3 days 39 39 12 2nd (15.5oC) ≥ 4 days 30 29 13 1st (14.6oC) ≥ 2 days 22 19 14 1st (14.6oC) ≥ 3 days 20 17 15 1st (14.6oC) ≥ 4 days 14 15
3.6.5.1 Estimating the added effect of extreme temperatures Generalised linear models (GLMs) were applied using a negative binomial distribution and DLNM to evaluate the effect of heat waves or cold spells on the risk of AMI hospitalisation (Gasparrini et al., 2010a). Heat waves were defined differently by regions, as the North and South Central Coast regions of Vietnam have different climate zones with distinct climate patterns. The climate in the North Central Coast region is humid and tropical monsoon climate with hot-dry summer and cold-wet winter while the South Central Coast region has a tropical savanna climate, characterised by dry weather and warm weather all year around (only summer) (Nguyen Chau Giang, 2012; Vietnamese Ministry of Education and Training, 2018a; 2018b). A DLNM was applied to investigate the delayed effects of heat waves for lags of seven days (0-7) and cold spells for a lag of 21 days (0-21). Different lags were chosen for heat waves and cold spells due to consistent evidence of an acute effect of heat or heat waves on morbidity, and a later and longer lag for cold temperature (Ye et al., 2012).
Chapter 3: Methodology 77
The GLMs model included AMI HAs as the outcome of interest, and long-term trend, seasonality, weekend and holiday, influenza-like illness incidence, and other meteorological factors, including mean temperature, as explanatory variables. Seasonality and long-term trend were adjusted by time (ordering number from 1 to 2,922 as the total number of studied period) and months as factor, weekend and holiday (as dummy variables [0 or 1]), and a spline function for daily mean temperature, relative humidity, air pressure and wind velocity, influenza-like illness with 3 degrees of freedom. A two-stage model was used to examine the influence of temperature extreme (heat waves or cold spells) separately after adjusting for the effect of other influences (seasonality, long term trend, mean temperature, humidity etc.), where the residuals from the first stage model were used in the second stage model.
Yt ~ negative binomial (μt)
Log (μt) = α + time + month+ ns (Tempt, 3df) + ns(RHt, 3df) + ns(APt, 3df) + ns(WSt, 3df) + ns(Inft, 3df) + Wendt + Holidayt + offset(log(pop))+ Et (1)
where t refers to the day of the observation; Yt is the observed daily AMI admission on day t; Tempt is the mean temperature on day t; RHt is the mean humidity on day t; APt is the mean air pressure on day t; WSt is the mean wind speed on day t;
Inft is the estimated mean influenza-like illness count on day t; ns(.) denotes the natural cubic spline functions; Wend is the weekend; Holiday is holiday; and Et is the residual of the models and log(pop) is the log scaled population put in the model (1) as an offset.
In the second model (Equation 2), the residuals of the previous model (Equation 1) were used to estimate the effect of heat waves or cold spells on AMI HAs.
fit2 = ŷ
Log (Et) = α + TEt,l + offset(log(fit2)) + εt (2)
Where TEt-l is a matrix created by DLNM for the temperature extreme event (heat wave or cold spell coded 1, otherwise coded 0 on day t with l lag days [21 days in this study]) and ŷ represents the fitted values from the first model (Equation 1).
One point worth explaining is why the mean temperature was included as a continuous variable in model 1, and heat waves or cold spells (the main point of interest in this part of the analysis) were subsequently included in model 2 as binary
78 Chapter 3: Methodology
variables. This is because the main objective was to determine the added effects of heat waves or cold spells, over and above that of the effects of ambient temperature. Thus, the effects of ambient temperature were differentiated and extreme temperature was added and ambient temperature therefore needed to be controlled for in the analysis.
The data analyses were carried out using “MASS” and “dlnm ver. 2.1.3” packages of R software (R Foundation for Statistical Computing, version 3.2.5 ( Gasparrini & Armstrong 2015; Venables & Ripley, 2002).
Further details and the results of the analysis are presented in Chapter 7.
3.6.6 Sensitivity analysis A number of sensitivity analyses were implemented in the study to examine the effect of ambient temperature or extreme temperature on AMI HAs. Various splines were fitted with different degrees of freedom of temperature in cross-basis (from three to six) to try and obtain a good picture of the association between temperature and AMI HAs. Cross-basis is a function of “dlnm package” in the R software, which initially generates the basis matrices for exposure-response and lag (i.e. delayed)- response. These one-dimensions basis matrices are then combined through a special tensor product in order to in order to create the cross-basis, which specifies the exposure-lag-response dependency simultaneously in the two dimensions. The two- dimension matrix can be included in a model formula to fit distributed lag linear (DLMs) and non-linear models (DLNMs) (Gasparrini & Armstrong 2015).
The degree of freedom of other meteorological variables was changed (from three to six), and the maximum lag in the DLNM was also changed (from seven to 30 days). Moreover, the analysis was repeated without data for year 2013 due to its unusual trend compared to other years in the data set. Natural splines and polynomial splines were also fitted. Furthermore, the robustness of the analyses was also checked using various methods, such as conducting case-crossover analysis or a single one- stage model (Guo et al., 2011; Mohammadi et al., 2018; Tong et al., 2012; J. Yang et al., 2017).
Case-crossover analysis is a special type of case-control study, which is suitable for investigating the association between transition exposure and acute health outcomes (Gordis, 2009; Tong et al., 2012). The characteristics of the study design
Chapter 3: Methodology 79
include self-matching of cases, comparisons between different points of time, and time-stratified control selection (Levy, Lumley, Sheppard, Kaufman, & Checkoway, 2001; Maclure, 1991). This type of approach can reduce the effects of seasonality, long-term trends, and other confounders from individual time-invariant characteristics (Levy et al., 2001; Lumley & Levy, 2000). However, this case-crossover analysis could only examine the effect of temperature on AMI HAs on the date of exposure and cannot address the delayed effects of temperature exposure. Therefore, it will not be reasonable to compare the results of case-crossover analysis and those applying the DLNM model (which includes both non-linear effect and the delayed effect simultaneously) (Gasparrini at al., 2010a).
The analysis compares each person’s exposure in a time period just prior to a case-defining event with the person’s exposure at other times. In this study, the date of hospitalisations was defined as the case day, with the same days of other weeks in the same month and year used as control days (three control days for a case day) (Figure 3.8). These data were analysed using conditional logistic regression analysis.
Figure 3.8 Control selection strategies
Regarding lag effects for the DLNM model, the analysis examined the association between HAs and temperature, not only on the day of hospitalisation (i.e., lag 0) but also the temperature on the days prior to the day of hospitalisation (i.e., lag1– 30). The longest lag effect to be analysed was lag 30 (for sensitivity analysis). However, we chose to highlight the maximum lag for DLNM of lag 21 (21 days prior to day of hospitalisation). This was due to the fact that previous studies suggested that this length of time is more than sufficient to capture the impact on mortality due to both hot and cold effects (Tawatsupa, Dear, Kjellstrom, & Sleigh, 2014). The results of the sensitivities analyses are presented in the Appendices K, L, O, S, and T.
80 Chapter 3: Methodology
Chapter 4: Descriptive Statistics of Acute Myocardial Infarction Patients and the Factors Associated with Delays Involved in First Medical Centre Admissions
This chapter provides an overview of the study population. The chapter also presents the descriptive statistics of the acute myocardial infarction (AMI) patients recruited in this research and provides evidence of several factors associated with delay times in hospital arrival among this population.
4.1 INTRODUCTION
Approximately 40-60% of AMI sufferers die within the first hour of the disease onset (Taghaddosi et al., 2010). However, early restoration of coronary flow by thrombolytic therapy or primary angioplasty, when indicated, reduces the risk for AMI death and prevents the development of complications (Giugliano & Braunwald, 2003). Subsequently, studies have shown that the efficacy of thrombolytic therapy and early angioplasty depend directly on the time between the appearance of the first symptoms of AMI and the provision of these treatments. Unfortunately, pre-hospital delay among patients with AMI is very long, with median intervals being above 120 minutes in most studies (Andrikopoulos et al., 2007; Brokalaki et al., 2011; Goldberg et al., 2002; Pipilis, Nanas, & Makris, 1995). In Vietnam, according to figures from the Vietnam National Heart Institute, only 2% of AMI patients arrive at the hospital within two hours (the "golden time" period), while the number of patients arriving within 12 hours was about 40%, leaving roughly 60% of the patients hospitalised too late (Vietnam National Heart Institute 2015). Those who arrive at hospital late are at a higher risk of death or having severe complications as a result of the disease (Anderson & Morrow, 2017).
Delayed hospital arrival after experiencing AMI signs/symptoms has been explained by several factors, including demographic, social, clinical, and emotional factors impacting on the late hospital arrival of patients with AMI (Brokalaki et al.,
Chapter 4: Descriptive Statistics of Acute Myocardial Infarction Patients and the Factors Associated with Delays Involved in First Medical Centre Admissions 81
2011). AMI patients who are older, female, not married, have a lower income, have diabetes, and are without a companion during the event, are generally associated with late attendance at hospitals (Li & Yu, 2017; Momeni et al., 2012; H. Nguyen et al., 2018; Nilsson, Mooe, Söderström, & Samuelsson, 2016; Taghaddosi et al., 2010). Therefore, the main aim of this chapter is to assess the association between several demographic and disease status variables with delayed hospital attendance after AMI symptoms’ onset in a study population located in Central Vietnam. To date, there are limited official publications in Vietnam regarding the issue of pre-hospital delay in Vietnamese patients hospitalised due to AMI. As far as I am aware, this is the second study about this concern; with the first one, which was implemented in Hanoi, under review (H. Nguyen et al., 2018). Therefore, research on this issue is highly valuable.
The identification of the delay characteristics and associated factors will help to inform policy decisions contributing to early hospital arrival, which will lead to improvements in terms of morbidity or mortality for AMI patients. Descriptive statistics of the study population are also presented.
The retrospective study design used all 3,328 AMI confirmed medical records of all AMI patients who were residents in Khanh Hoa, Thua Thien Hue, and Quang Binh and hospitalised in the regions, collected from 1st January 2008 to 31st December 2015. Among those AMI cases, only AMI cases whose medical record contained information on pre-hospital delayed time were used to estimate the delay time and its associated factors, leaving 3,256 cases used for this analysis.
In order to characterise the AMI population in the Central Coast of Vietnam, a simple statistical analysis was used to calculate mean, standard deviation, range, percentage, Chi-square test, t-test, etc. Logistic regression was used to examine the factors associated with delays in hospital arrival time (Kirkwood et al., 2003; Miller & Miller, 2010). Information on pre-hospital delay time (e.g., < 6h, 6–12h, 13–24h, and > 24h) was transformed into binary variables using a 12 hour threshold as the criterion for separation. Along with bivariate analysis, a backward stepwise multivariate logistic regression analysis was also conducted to take into account the confounding effects among significant explanatory variables for the pre-hospital delay time. Further details about the analytic methods used in this chapter were discussed earlier in Sections 3.6.1 and 3.6.2 of Chapter 3.
82 Chapter 4: Descriptive Statistics of Acute Myocardial Infarction Patients and the Factors Associated with Delays Involved in First Medical Centre Admissions
4.2 DEMOGRAPHIC CHARACTERISTICS AND DISEASE HISTORY OF ACUTE MYOCARDIAL INFARCTION PATIENTS
There was a total of 3,328 admissions of local residents to the Hue Central Hospital (Thua Thien Hue Province), Vietnam-Cuba Friendships Hospital (Quang Binh Province), and Khanh Hoa General Hospital (Khanh Hoa Province) between 1st January 2008 and 31st December 2015. A total of 1,342 cases were from Khanh Hoa, while 1,274 cases were from Thua Thien Hue and 712 cases from Quang Binh. Demographic characteristics of the studied population are presented in Table 4.1.
Table 4.1 Demographic characteristics of AMI patients by provinces Characteristics Khanh Hoa Thua Thien Hue Quang Binh Total
No. of AMI HAs 1342 1274 712 3328 Incidence rates 11.13 11.17 8.16 10.15 (per 1,000,000) Gender*** Male 840 (62.6%) 742 (58.2%) 522 (73.3%) 2104 (63.2%) Female 502 (37.4%) 532 (41.8%) 190 (26.7%) 1224 (36.8%) Occupation*** Unemployed 960 (71.5%) 1032 (81.0%) 561 (78.8%) 2553 (76.7%) Working 382 (28.5%) 242 (19.0%) 151 (21.2%) 775 (23.3%) Health insurance*** 937 (69.8%) 1012 (79.4%) 628 (88.2%) 2577 (77.4%) Note: *p < 0.05, **p < 0.01 and ***p < 0.001; Unemployed = retired/lack of work capacity
The age of those admitted for AMI ranged from 21 to 105 years old, with a mean of 70.3 ± 13.6 years. Khanh Hoa’s patients (68.4 ± 13.6) were statistically significantly younger than the other two groups, with 70.05 ± 13.0 (in Quang Binh), and 72.45 ± 13.4 (in Thua Thien Hue) (p < 0.001). The proportion of patients whose ages were equal to or less than 65 in Khanh Hoa was 36.7%, compared to 29.6% in Quang Binh, and 27.6% in Thua Thien Hue (p < 0.001) (see Figure 4.1).
Chapter 4: Descriptive Statistics of Acute Myocardial Infarction Patients and the Factors Associated with Delays Involved in First Medical Centre Admissions 83
Khanh Hoa Thua Thien Hue Quang Binh
1000 72.4% 900 63.3%
800
700
600 36.7% 70.4% 500
AMI HAs AMI 400 27.6%
300 27.6% 200
100 0 Aged < 65 Aged ≥ 65 Age group Figure 4.1 Distribution of AMI patients by Age
The majority of the AMI patients were male, representing approximately two- thirds of all AMI patients (63.2%). Quang Binh had the highest proportion of males with 73.3%, while Thua Thien Hue was only 58.2%. The majority of the population were “unemployed” (retired or not working, occupied 76.7%), these proportions were 71.5%, 81%, and 78.8% in Khanh Hoa, Thua Thien Hue, and Quang Binh, respectively. More than two-thirds or above had health insurance (77.4%), the relevant percentages for Khanh Hoa, Thua Thien Hue, and Quang Binh, were 69.8%, 79.4%, and 88.2%, respectively. There were statistically significant differences for gender, occupational status, and having health insurance in the three studied provinces (p<0.001).
Most AMI cases presenting at the first medical centres (defined by where AMI patients initially visited after having AMI signs/symptoms) on the day they experienced AMI signs/symptoms (65.5%). The first medical centre that the patients visited included private clinics, commune health centres, city hospitals in the urban areas or district hospitals in rural areas, provincial level hospitals and central hospitals. Among those, only provincial level hospitals and central hospitals were able to provide AMI reperfusion treatment, which is recognised as a specific treatment for AMI cases. More than two-thirds of patients (63.3% and 74.6%) presented directly at the high- level hospitals in their provinces in Khanh Hoa and Thua Thien Hue, respectively,
84 Chapter 4: Descriptive Statistics of Acute Myocardial Infarction Patients and the Factors Associated with Delays Involved in First Medical Centre Admissions
while only half of the patients in Quang Binh (56.6%) chose high level hospitals. Moreover, AMI patients were rarely hospitalised at commune health centres and private clinics (less than 0.5% and 0.9% respectively).
Regarding AMI status and disease history, the majority of patients were ST- elevated myocardial infarction (STEMI), with 53.7% in Khanh Hoa, nearly 70% in Thua Thien Hue, and 60% in Quang Binh, of which the proportion of infarction in the right ventricle was higher in Khanh Hoa (8.9%), compared to those in Thua Thien Hue and Quang Binh (2.2% and 1.0%, respectively) (p < 0.001). Khanh Hoa seemed to be the province that had a greater proportion of patients with co-morbidities (70.6% vs 58.8% in Thua Thien Hue and 61.8% in Quang Binh) (p < 0.001). In particular, hypertension was diagnosed for 55% of AMI patients in Khanh Hoa compared to 28.9% of that in Thua Thien Hue and 30.2% in Quang Binh. Diabetes was diagnosed for 27.3% in Khanh Hoa AMI patients compared to 13.2% in Thua Thien Hue and 9.8% in Quang Binh. The proportion of AMI patients with current coronary heart diseases was similar at all three provinces (4.2%, 4.9%, and 2.3%, in Khanh Hoa, Thua Thien Hue, and Quang Binh, p = 0.083), as well those having pre-CVDs (65.2% vs 60.8% in Thua Thien Hue and 63.2% in Quang Binh, p = 0.072). More AMI patients had poor prognosis (Killip classification as III and IV) in Quang Binh (34.7%) than those in Khanh Hoa (21.0%) and Thua Thien Hue (23.6%), p < 0.001. Details of the health conditions and hospitalisation of the studied AMI patients are presented in Table 4.2.
Chapter 4: Descriptive Statistics of Acute Myocardial Infarction Patients and the Factors Associated with Delays Involved in First Medical Centre Admissions 85
Table 4.2 Hospital access and AMI conditions of patients by provinces Characteristics Khanh Hoa Thua Thien Hue Quang Binh Total
First medical centre access*** CHCs 7 (0.5%) 6 (0.5%) 2 (0.3%) 15 (0.5%) District/city hospitals 469 (34.9%) 291 (24.1%) 301 (42.6%) 1061(32.6%) Provincial hospitals 850 (63.3%) 106 (8.8%) 39 (5.5%) 995 (30.6%) Central hospitals 0 (0%) 794 (65.8%) 363 (51.3%) 1157 (35.5%) Private clinics 16 (1.2%) 10 (0.8%) 2 (0.3%) 28 (0.9%) AMI status*** STEMI 721 (53.7%) 837 (69.3%) 425 (60.1%) 1983 (60.9%) AMI positions Anterior*** 539 (52.8%) 641 (64.2%) 319 (64.2%) 1499 (59.6%) Lateral 36 (3.5%) 39 (3.9%) 17 (3.4%) 92 (3.7%) Septal*** 160 (15.7%) 89 (8.9%) 73 (14.7%) 322 (12.8%) Inferior*** 495 (48.5%) 358 (35.9%) 173 (34.8%) 1026 (40.8%) Right ventricle*** 91 (8.9%) 22 (2.2%) 5 (1.0%) 118 (4.7%) Pre-CVDs 875 (65.2%) 734 (60.8%) 447 (63.2%) 2056 (63.1%) Co-morbidity*** 947 (70.6%) 710 (58.8%) 437 (61.8%) 2094 (64.3%) CHDs 40 (4.2%) 35 (4.9%) 10 (2.3%) 85 (4.1%) Hypertension*** 521 (55.0%) 205 (28.9%) 132 (30.2%) 858 (41.0%) Diabetes*** 295 (27.3%) 94 (13.2%) 43 (9.8%) 396 (18.9%) Blood lipid abnormality* 27 (2.9%) 10 (1.4%) 5 (1.1%) 42 (2.0%) Killip class*** I 918 (68.4%) 810 (67.1%) 399 (56.4%) 2127 (65.3%) II 142 (10.6%) 112 (9.3%) 70 (9.9%) 324 (10.0%) III 60 (4.5%) 77 (6.4%) 16 (2.3%) 153 (4.7%) IV 222 (16.5%) 208 (17.2%) 222 (31.4%) 652 (20.0%) Note: *p < 0.05, **p < 0.01 and ***p < 0.001 CHCs: commune health centres; STEMI: ST-segment elevation myocardial infarction; CVDs: cardiovascular diseases; CHDs: coronary heart diseases
In terms of treatment for AMI, all of the patients in this research received either PCI (33.9%) or medications (66.1%), of which the proportion of patients who received PCI differed among the provinces; the proportions in Khanh Hoa and Thua Thien Hue were 33.1% and 48.7%, respectively, whereas no patients received PCI in Quang Binh. The proportion of patients discharged with a bad condition (as literately recorded in the hard-copy medical records, referring to a poor prognosis for the patient’s condition) or those who died, was highest in Quang Binh, accounting for 56.4% of the total AMI patients in this province. In Khanh Hoa and Thua Thien Hue these proportions were 30.1% and 37.7%, respectively, with p < 0.001. A summary of treatment and health status at discharge of the studied population is provided in Table 4.3.
86 Chapter 4: Descriptive Statistics of Acute Myocardial Infarction Patients and the Factors Associated with Delays Involved in First Medical Centre Admissions
Table 4.3 Treatment and health status at discharge of AMI patients by provinces Characteristics Khanh Hoa Thua Thien Quang Binh Total Hue Treatment types*** Medication 689 (51.3%) 809 (66.9%) 712 (100.0%) 2210 (67.7%) PCI 653 (48.7%) 400 (33.1%) 0 (0.0%) 1053 (32.3%) Health status at discharged*** Good/positive 938 (69.9%) 793 (62.2%) 311 (43.7%) 2042 (61.4%) prognosis Bad/poor prognosis 203 (15.1%) 459 (36.0%) 313 (44%) 975 (29.3%) Died 201 (15.0%) 22 (1.7%) 88 (12.4%) 311 (9.3%) Note:*p < 0.05, **p < 0.01 and ***p < 0.001 PCI: –percutaneous coronary intervention
4.3 FACTORS ASSOCIATED WITH THE DELAY TIME OF FIRST MEDICAL CENTRE ADMISSIONS OF ACUTE MYOCARDIAL INFARCTION PATIENTS
For cases with information on time from first signs/symptoms of AMI to first medical centre, of the total (3,256) 46.1% of the patients arrived at the first medical centre later than 12 hours of having the AMI signs/symptoms onset. Quang Binh, in general, had a higher proportion of AMI patients who delayed their hospitalisation from the first signs/symptoms of AMI compared to others living in Thua Thien Hue and Khanh Hoa. Patients who lived in Quang Binh province (p < 0.001), unemployed (p < 0.001), and had health insurance (p < 0.001) were more likely to report a delayed hospital arrival of more than 12 hours (Table 4.4). Moreover, patients who were initially admitted at provincial or central level hospitals were more likely to be hospitalised prior to 12 hours from the symptoms onset (p <0.001). Regarding disease conditions, patients with: Non-STEMI status (p < 0.05); who had a lower ranking of Killip classification (class I) (p < 0.01) and at least one co-morbidity (p < 0.001) were more likely to arrive at the hospital after a time period of more than 12 hours. There was no significant difference in terms of late hospital arrival time between those patients who had diabetes or did not (49.8% vs 48.5%, p > 0.05) (Table 4.4).
A summary of delayed hospitalisation times of studied AMI patients is presented in Table 4.4.
Chapter 4: Descriptive Statistics of Acute Myocardial Infarction Patients and the Factors Associated with Delays Involved in First Medical Centre Admissions 87
Table 4.4. Distribution of AMI patients with respect to delay referral to first medical centre by provinces Provinces < 6h 6-12h 13-24h >24h n % n % n % n % Thua Thien 484 40.1 190 15.7 235 19.5 298 24.7 Hue Quang Binh 230 32.5 76 10.7 193 27.3 208 29.4 Khanh Hoa 625 46.6 151 11.3 293 21.8 273 20.3 Total 1339 41.1 417 12.8 721 22.1 779 24.0
Table 4.5 provides the investigated demographic and disease characteristics of AMI patients by referral time. A late hospital arrival (>12 h) was found to be higher for females compared to males (50.5% in females vs. 43.5% in males, p < 0.001). The average age of patients arriving later than 12 hours after the disease onset was slightly higher than those arriving after less than 12 hours (72.2 ± 13.3 vs 68.8 ± 13.7, p<0.001), although there was considerable uncertainty or variability around these estimates (Figure 4.2).
Figure 4.2 Association between Age and delay referral to first medical centre of AMI patients
(Mean±SD, p < 0.001)
88 Chapter 4: Descriptive Statistics of Acute Myocardial Infarction Patients and the Factors Associated with Delays Involved in First Medical Centre Admissions
Table 4.5 Distribution of AMI patients with respect to related factors and delay referral to first medical centre Background factors ≤ 12h > 12h ORs p value n % n % Total 1756 53.9 1500 46.1 Gender <0.001 Female 591 49.5 603 50.5 1.33 Male 1165 56.5 897 43.5 - Province <0.001 Thua Thien Hue 674 55.8 533 44.2 1.11 Quang Binh 306 43.3 401 56.7 1.83 Khanh Hoa 776 57.8 566 41.2 - Occupational status <0.001 Unemployed 1279 51.3 1215 48.7 1.59 Working 477 65.6 285 37.4 - Having Health Insurance <0.001 No 452 62.3 274 37.7 0.65 Yes 1304 51.5 1226 48.5 - First medical centre admissions <0.001 Lower level hospitals 510 46.2 594 53.8 1.60 Central/provincial 1246 57.9 906 42.1 - hospitals AMI status 0.01 Non-STEMI 651 51.1 622 48.9 1.20 STEMI 1105 55.7 878 44.3 - Killip classification <0.001 IV 393 60.3 259 39.7 0.78 III 66 43.1 87 56.9 1.56 II 146 45.1 178 54.9 1.44 I 1151 54.1 976 45.9 - Pre - CVD 0.08 Yes 1085 52.8 971 47.2 1.14 No 671 55.9 529 44.1 - Co-morbidity <0.001 Yes 1057 50.5 1037 49.5 1.48 No 699 60.2 463 39.8 - Diabetes 0.65 Yes 853 50.2 845 49.8 1.05 No 204 51.5 192 48.5 Hypertension 0.43 Yes 615 49.8 621 50.2 1.07 No 442 51.5 416 48.5 Blood lipid abnormality 0.24 Yes 1032 50.3 1020 49.7 1.45 No 25 59.5 17 40.5 CHDs 0.84 Yes 1015 50.5 994 49.5 0.96 No 42 49.4 43 50.6 Note: STEMI: ST-segment elevation myocardial infarction; CVDs: cardiovascular diseases, Unemployed: retired/lack of working capacity, CHDs: Coronary heart diseases
Chapter 4: Descriptive Statistics of Acute Myocardial Infarction Patients and the Factors Associated with Delays Involved in First Medical Centre Admissions 89
Table 4.6 summarises the main findings of the univariate and multivariate regressions. In the uni-variable analysis, gender, age, region, first medical centre admitted, Killip ranking, or co-morbidity status were significantly related to delays in pre-hospital arrival time for AMI patients. Whereas gender, age, living in Quang Binh, first medical centre admitted, highest Killip ranking, and having a co-morbidity status remained significantly related to delays in pre-hospital arrival time for AMI patients in multivariate regression. The risk of delayed hospital arrival was almost 1.2 times greater among patients who were female (RR 1.20, 95% CI 1.03–1.40, p = 0.02). Moreover, the risk of delayed access to a medical centre of more than 12 hours increased by 2% (95% CI 1.01–1.02, p < 0.001) for each yearly increase in age. For those who lived in Quang Binh, the risk of delayed hospital arrival was double (RR: 2.03, 95% CI 1.68–2.47, p < 0.001) in comparison with patients living in Khanh Hoa province. In terms of first medical centre admitted, patients who were initially admitted at lower level hospitals were likely to delay their arrival (more than 12 hours). The risk of delayed hospital arrival was approximately 55% (95% CI: 1.33–1.80, p < 0.001) higher if patients were primarily hospitalised at lower level hospitals than those arriving at a provincial or central level hospital. Similarly, there was a roughly 63% (95% CI: 0.52–0.76, p < 0.001) reduction of risk of delayed hospital arrival in patients ranked as Killip 4 (patients with a severe health condition) in comparison to those ranked as Killip 1 (patients remaining in the normal range of respiratory- cardiovascular conditions). Moreover, AMI patients with at least one comorbidity had a 36% (95% CI: 1.16–1.59, p < 0.001) higher risk of delayed hospital arrival than those without a comorbidity.
AMI status (STEMI or Non-STEMI) was the factor shown to be a potential factor associated with delayed hospital arrival time in uni-variable logistic regression, as patients with Non-STEMI were likely to increase the risk of delayed hospital arrival time by 20% (RR 1.20, 95% CI: 1.04–1.39, p = 0.01). Similarly, the risk of delayed hospital arrival was approximately 59% (95% CI: 1.35–1.89, p < 0.001) higher if the patients were “unemployed” than “employed”; there was a roughly 35% (95% CI: 0.54–0.76, p < 0.001) reduction of risk of delayed hospital arrival in patients without health insurance in comparison to those having health insurance. However, these factors did not remain significantly associated in the multi variable logistic regression (OR = 1.16 (95%CI: 0.99 – 1.35, p = 0.06) for AMI status; OR = 0.86 (95%CI: 0.67 –
90 Chapter 4: Descriptive Statistics of Acute Myocardial Infarction Patients and the Factors Associated with Delays Involved in First Medical Centre Admissions
1.10, p = 0.23) for employment status; and OR = 1.10 (95%CI: 0.91 – 1.32, p = 0.29 for health insurance ownership). In addition, other factors that were examined for their relationships with delayed hospital arrival time but showed no significant association included patients with hypertension (OR = 1.07 (95%CI: 0.90 – 1.28, p = 0.43)), diabetes (OR = 1.05 (95%CI: 0.85 – 1.31, p = 0.65), blood lipid abnormality (OR = 1.45 (95%CI: 0.78 – 2.71, p = 0.24), having CHDs (OR = 0.96 (95%CI: 0.62 – 1.48, p = 0.84), and having CVD in disease history (OR = 1.14 (95%CI: 0.98 – 1.31, p = 0.08).
Table 4.6 Odd ratios of delay time for first medical centre admissions in AMI patients with respect to significant related factors Associated Univariable p Multivariable p factors regressions value regression value Crude 95% CI Adjusted 95% CI ORs ORs Age 1.02 (1.02–1.03) <0.001 1.02 (1.01–1.02) <0.001 Gender Female 1.33 (1.15–1.53) <0.001 1.20 (1.03–1.40) 0.02 Male - - - - Province <0.001 <0.001 Thua Thien Hue 1.11 (0.95–1.30) 0.20 1.16 (0.99–1.38) 0.10 Quang Binh 1.83 (1.52–2.20) <0.001 2.03 (1.68–2.47) <0.001 Khanh Hoa - - - - First medical centre admissions Lower level 1.60 (1.38–1.85) <0.001 1.55 (1.33–1.80) <0.001 hospitals Central/provincial - - - - level hospitals Killip classification <0.001 <0.001 IV 0.78 (0.65–0.93) 0.006 0.63 (0.52–0.76) <0.001 III 1.56 (1.12–2.17) 0.009 1.23 (0.87–1.72) 0.28 II 1.44 (1.14–1.82) 0.002 1.14 (0.89–1.45) 0.30 I - - - - Comorbidity Yes 1.48 (1.28–1.71) <0.001 1.36 (1.16-1.59) <0.001 No - - - - AMI status Non-STEMI 1.20 (1.04–1.39) 0.01 1.16 (0.99-1.35) 0.06 STEMI - - - - Note: STEMI: ST-segment elevation myocardial infarction. Variables adjusted for in the multi-variable regression include age, gender, province, first medical centre admissions, Killip classification, Comorbidity, AMI status, occupational status, having health insurance.
Chapter 4: Descriptive Statistics of Acute Myocardial Infarction Patients and the Factors Associated with Delays Involved in First Medical Centre Admissions 91
4.4 DISCUSSION
The studied AMI population shared similar characteristics with other AMI patients in Vietnam ( Ha & Nguyen, 2018; H. Nguyen et al., 2018) with the majority of the population being the elderly, more than two thirds were males, and less than one third were non-STEMI. Among the total of 3,328 AMI hospital admissions (HAs) in Khanh Hoa, Thua Thien Hue, and Quang Binh, 63.2% were male, 68.3% were those at aged 65, and 76.7% were “unemployed”. The proportion of patients with health insurance was 77.4%. Those diagnosed as STEMI accounted for 60.9%, those with at least one co-morbidity was 64.3%, of which those with diabetes was 18.9%. Moreover, AMI patients with a pre-history of CVDs was 63.1%.
The results of this study also indicate that Vietnamese patients experienced a considerable pre-hospital delay after the onset of AMI symptoms, with nearly half (46.1%) experiencing a delay time longer than 12 hours. This figure is quite similar to the estimate of T. Nguyen et al. (2018) for AMI patients in Hanoi, Vietnam in 2010, with 42% patients delayed ≥ 12 hours. The proportion of patients who delayed less than six hours was 45% and from six to less than 12 hours was 13%. In comparison, these estimates are much higher than comparable estimates in the USA, where the prevalence of AMI patients hospitalised after 12 hours was only 6.9% (Makam et al., 2016).
In this study, the cut-off point was chosen at more than 12 hours to separate “in- time” or “referral delay” in terms of pre-hospital delay. This is the time recommended by Vietnamese Ministry of Health (Vietnamese Ministry of Health 2013) as the preferable time to provide reperfusion treatment for AMI patients. However, it is acknowledged that this cut-off is longer than “common” delay cut-offs (two hours or even six hours) used in other studies (M. R. Lee et al., 2016; Makam et al., 2016; Momeni et al., 2012; H. Nguyen et al., 2018), and several researchers measured delay time as a continuous variable (Ängerud, Sederholm Lawesson, Isaksson, Thylén, & Swahn, 2017; Dianati, Mosavi, Hajibagheri, & Alavi, 2010; P. W. Li & Yu, 2017). These studies used much shorter cut-off times to define the delay time, taking into account that the thrombolytic therapy and early angioplasty success rates are higher within the first 90 min of the AMI event (Diercks et al., 2008). Specifically, early angioplasty is superior to thrombolytic therapy if it is performed within the first 90 minutes of the episode, but this advantage disappears after the first 180 minutes
92 Chapter 4: Descriptive Statistics of Acute Myocardial Infarction Patients and the Factors Associated with Delays Involved in First Medical Centre Admissions
(Diercks et al., 2008; Keeley et al., 2003; White & Chew, 2008). In line with this matter, there is a need to enhance the knowledge of local residents about AMI, especially the importance of an emergency hospitalisation after AMI onset, in addition to the severity of the disease, its traditional and untraditional risk factors, and early signs/symptoms of the disease. Tertiary preventative approaches relating to in-time hospital arrival could contribute to more successful recovery and survival of AMI patients.
This study found that there were statistically significant associations between the pre-hospital delay among AMI patients and demographic factors and health conditions, including gender, age, and location, first medical centres admitted, ranking of Killip classification, and having a co-morbidity. These results support previous research relating to the associated factors for delayed pre-hospital time, which has been found to be one of the most important elements affecting the specific treatment for AMI patients and their recovery (Dianati et al., 2010; M. R. Lee et al., 2016; Makam et al., 2016; Momeni et al., 2012). The current study also found potential explanatory factors that have not been well-documented in the literature, in particular, the Killip classification of AMI patients (from I to IV). The Killip classification is a valuable prognostic stratification for patients with AMI, taking into account physical examination and the development of heart failure in order to predict and stratify their risk of mortality. Individuals with a low Killip class have better prognosis after a myocardial infarction than individuals with a high Killip class (H. Cheng & Yen, 2010). The findings of this study support evidence from previous studies of significant linkages between prolonged prehospital time and severity of the symptoms or perceiving symptoms not to be so serious (Momeni et al., 2012; Taghaddosi et al., 2010). However, the records of Killip classification for the current study were obtained from the final hospitals where patients were admitted; therefore, the delays patients experienced in reaching these hospitals could have affected this ranking, thus confounding the relationship between the delay time and the severity of the symptoms.
A significant difference was found in arrival time by gender after controlling for other variables in the multi-variable regression, in which women delayed their arrival at the hospital after AMI onset when compared with men. The risk of AMI HAs in the current study was 20% (95%CI: 3%–40%) higher for females than males. This result was similar to the results found from other studies (Brokalaki et al., 2011; Gibler et
Chapter 4: Descriptive Statistics of Acute Myocardial Infarction Patients and the Factors Associated with Delays Involved in First Medical Centre Admissions 93
al., 2002; Graham, 2016; Nguyen, Ha, et al., 2014; Richards, Reid, & Watt, 2002; Ting et al., 2008), but was in contrast with studies that showed no difference in gender (Brokalaki et al., 2011; Momeni et al., 2012; Zerwic, Ryan, DeVon, & Drell, 2003). In the multi-variable regression, both gender and age remained significant; therefore, gender seemed to be the real modification for pre-hospital delay period for AMI. In addition, in the Vietnamese cultural context, where the role of women in the family is still less important than that of men, women may be more likely to delay their health care seeking in general (Treleaven et al., 2016).
The results show that the elderly group was more likely to arrive at the first medical centre 12 hours after AMI occurrence. This finding is in line with many results of studies confirming an age modification effect on the delay hospital arrive time. A study by Taghaddosi et al. (2010) of 200 AMI patients and another study by Crumlish et al. (2000) of 2,000 AMI cases found that age was positively and significantly associated with delay in arrival time. The results of these findings may be due to a higher pain threshold in older people, or an increase in personal knowledge and experience that causes the delay for referring to hospital (Crumlish et al., 2000; Taghaddosi et al., 2010).
Another significant result was that there was a longer hospital delay in AMI patients who lived in Quang Binh and Thua Thien Hue compared to Khanh Hoa. This was assumed to be due to AMI patients in Khanh Hoa being significantly younger than those in Quang Binh and Thua Thien Hue (68 vs 72 and 70 in mean age, respectively), as higher age related to higher delay rate, as mentioned earlier. However, in the multi- variable regression, both location and age remained significant; therefore, provinces were likely to have other characteristics that could affect the delayed pre-hospital arrive time due to AMI. This could have arisen because Quang Binh is a province more economically disadvantaged than Khanh Hoa regarding lower average SES, larger land area, and a more rural population. Comparison of provincial characteristics between the two provinces are shown in Appendix G. Longer delay times may have experience Quang Binh because patients may not have had transportation to get to a medical facility and they may have needed to travel further to get to the medical facility. However, our collected data were not sufficient to determine the precise reasons for differences between provinces in delay time.
94 Chapter 4: Descriptive Statistics of Acute Myocardial Infarction Patients and the Factors Associated with Delays Involved in First Medical Centre Admissions
The results also revealed that AMI patients who firstly presented at a lower level hospital (multi-variable regression) were more likely to delay their hospital arrival after onset. This can be explained by the fact that in the Vietnamese context, reperfusion treatment for AMI is only performed at provincial or central level hospitals. Thus, if AMI sufferers are hospitalised at a lower level hospital, they are then referred to high level hospitals for treatment.
In terms of severity of health condition, AMI patients with the most severe health condition (life-threatening–ranking as Killip 4) were less likely to experience a delay in hospital arrival. This may be because the family of the patient was likely to be in a hurry to send them to hospital if their symptoms were perceived to be severe and life- threatening. On the other hand, having at least one co-morbidity was associated with an increase in pre-hospital delay for AMI patients. A possible reason for this is that the co-morbidity reduces the pain threshold for these patients. Moreover, it could be that having a co-morbidity resulted in patients making less of a link to their signs/symptoms to AMI, instead they might perceive AMI’s signs/symptoms to be part of the co-morbidity (normally not an emergency for hospitalisation). A history of having diabetes and hypertension has been linked to a prolonged delay in HAs in AMI patients (Brokalaki et al., 2011; Dracup & Moser, 1997; Gibler et al., 2002; Goldberg et al., 2002; Taghaddosi et al., 2010). Several studies have argued that diabetic neuropathy in patients with diabetes mellitus eliminates the feeling of pain. However, associations with diabetes and hypertension as co-morbidities were not significant in the current study.
There appears to be good evidence or agreement that factors such as lower social economic status, having lower literacy status, long distance between the patient’s residence and the nearest hospital, absence of a companion/attendant/escort during the AMI, and the pattern of symptoms are associated with delayed hospital arrival in AMI patients (Brokalaki et al., 2011; Herlitz et al., 2010; Perkins-Porras, Whitehead, Strike, & Steptoe, 2009; Perry, Petrie, Ellis, Horne, & Moss-Morris, 2001; Taghaddosi et al., 2010). These factors could not be assessed in the current study because the information was not available in the hard-copy medical records.
There are a number of limitations of this study. First, the data were collected from hard copy medical records; therefore limiting the number of explanatory factors included in the analysis. Moreover, the centre-based sample size may have reduced the
Chapter 4: Descriptive Statistics of Acute Myocardial Infarction Patients and the Factors Associated with Delays Involved in First Medical Centre Admissions 95
ability to generalise the findings to the whole of the regions. However, the selected centres were the three biggest general hospitals in each studied province, and were therefore expected to cover 80% of hospitalised AMI patients of these provinces. Thus, these findings are quite comparable with the recent study on the pre-hospital delay in AMI hospitalised at the Vietnam National Heart Institute – the leading institute for cardiovascular disease (CVDs) treatment in the country (H. Nguyen et al., 2018). In T. Nguyen et al.’s (2018) study, the authors shared a similar conclusion of gender being an important modification of this delay, as patients who had a delay time over six hours were more likely to be female compared to male. They also found no significant relationships between age variation, ethnicity, health insurance property, medical history, AMI status (STEMI/Non-STEMI), symptoms, and para-clinical tests and hospital delay (H. Nguyen et al., 2018).
The estimates of delayed pre-hospital time and patients’ delay time were not able to be calculated separately for the first medical centre and extent of inter-medical centre referral time due to a lack of information. Furthermore, due to the retrospective design of the study, systematically collected and/or recorded information about the time of onset of symptoms suggestive of AMI was not available in hospital medical records. This information was abstracted from notes written by different physicians and emergency personnel, which may have been collected and recorded in a non- standardised manner. Hence, there was the possibility of misclassification bias on the time interval from the onset to the initial hospitalisation. Finally, yet importantly, AMI patients who did not seek treatment or died before arriving at hospital were not included in this study. Those biases might reduce the precision of the estimates of the found associations.
Another limitation of this study was that data regarding the time taken to refer patients from lower-level hospitals (i.e., commune health centres and district hospitals) to provincial or central hospitals was not able to be collected. Therefore, future studies should measure the time interval from the AMI onset to presentation at the hospital, accounting for the referral time from lower-level hospitals to higher ones. Alternatively, research focusing only on the AMI patients who initially visit high-level hospitals is likely to obtain more accurate information about potential factors associated with the delayed pre-hospital arrival of AMI cases. Moreover, valuable information can be gained by interviewing or surveying patients at an early stage of
96 Chapter 4: Descriptive Statistics of Acute Myocardial Infarction Patients and the Factors Associated with Delays Involved in First Medical Centre Admissions
hospitalisation in order to reduce the level of information bias where the researcher directly examines the delayed pre-hospital time and the related factors. The selection bias and information bias may affect the precision and/or lead to underestimation of the observed associations.
4.5 CONCLUSION
In this chapter, 46.1% of AMI patients were found to have presented to first medical centres after 12 hours from the onset of signs/symptoms. Consequently, their survival rate is low and they have a higher risk of getting severe complications after cardiac necrosis. A number of independent predictors of pre-hospital delay were also found, including: gender, age, location, level of the first medical centres admitted, ranking of Killip classification, and having at least one co-morbidity. These findings demonstrate an urgent need for health education for the general population about AMI, especially in regard to in-time referral for AMI treatment. These findings can be used to help plan educational programmes of secondary and tertiary prevention so as to decrease the pre-hospital delay of patients with AMI and to provide high-risk groups for AMI with the necessary knowledge about early hospitalisation after having signs/symptoms of AMI. The following chapter examines the seasonality of HAs due to AMI in Central Coast regions of Vietnam.
Chapter 4: Descriptive Statistics of Acute Myocardial Infarction Patients and the Factors Associated with Delays Involved in First Medical Centre Admissions 97
Chapter 5: Analysis of Seasonality in Hospital Presentations for Acute Myocardial Infarction in Central Vietnam
This chapter explores variations in admissions for AMI in the Central Coast of Vietnam. In particular, it examines the trends and seasonality of acute myocardial infarction (AMI) occurrence in hospital admissions (HAs).
5.1 INTRODUCTION
An understanding of the seasonal variation can assist in identifying potentially preventable determinants of the disease. An increase in AMI occurrence or mortality due to AMI in colder weather has been reported in various locations, for example, in the USA (Mannino & Washburn, 1989; Spencer et al., 1998), Great Britain (Marchant, Ranjadayalan, Stevenson, Wilkinson, & Timmis, 1993), India (Thakur, Anand, & Shahi, 1987), China (J. Yang et al., 2017), Bangladesh (Khan & Halder, 2014), and Australia (Enquselassie, Dobson, Alexander, & Steele, 1993). In contrast, studies in Taiwan (Ku et al., 2008), Spain (Fernández- García et al., 2015) and Slovenia (Ravljen et al., 2018) showed no correlation seasonal variation and AMI occurrence. The higher risk of AMI deaths or incidence in winter is potentially due to the cold effects on the human body, including changes in sympathetic nervous activity, greater tendency to clot in the circulatory system, increasing frequency of abrupt rupture of atherosclerotic plaques, etc. (Khan & Halder, 2014; Wolf et al., 2009). However, the magnitude of the effects depends not only on the analytical approach but also the studies’ geographic location and local population, with a diversity on their acclimated human body and adaptive cultures and behaviours. Hence, it remains important to ascertain the seasonality of the disorder occurrence in various population settings.
Seasonal patterns of AMI onset have not been quantified in the Vietnamese setting. The Central Coast of Vietnam is known for its cold winters and hot dry summers, making it an excellent location to examine such seasonality. The main aim of this chapter is to explore medium term trends and seasonality of HAs of patients with AMI in three provinces in Vietnam that vary in latitude and climatic characteristics. The influence of different climate zones, gender, and age on the seasonality of AMI admissions is also examined.
Chapter 5: Analysis of Seasonality in Hospital Presentations for Acute Myocardial Infarction in Central Vietnam 99
Preparing for these analyses, a time-series dataset was created from the dataset of a total of 3,328 AMI cases, where each unit was an AMI case consisting of a single day for every day from 1st January 2008 to 31st December 2015 (2,922 days). For each day, the total number of AMI HAs that occurred in each province and measures of time-varying variables (i.e., 24-hour mean temperature, relative humidity and so on) were recorded.
The AMI HAs were then aggregated by year for long-term trend analysis, and aggregated AMI HAs by season and months for seasonality analysis. To examine the trend of AMI HAs during the studied period, the incident rate ratio (IRR) and 95% confidence intervals (CIs) were calculated using a generalised linear regression model (GLMs), including year (treated as a continuous variable) and month (as a categorical variable, of which July was selected as the reference month) for each province.
Similarly, to determine whether certain seasons were associated with higher risks of hospitalisation, IRR and 95% CIs were calculated using a GLMs, with summer as the reference group for each province. Seasons were defined based on the meteorological seasons and the length of each season was also adjusted to 91 days. Comparisons were made between the seasonality of AMI HAs for gender and by age groups. Details of long-term trends and seasonal analysis were presented in Section 3.6.3 of Chapter 3.
5.2 TRENDS OF HOSPITAL ADMISSIONS IN THE THREE PROVINCES
In general, all provinces appeared to reveal significant and increasing trends (p < 0.001); however, their patterns varied differently over the study period. Figure 5.1 shows the trends of AMI HAs for each province. The AMI HAs of Khanh Hoa showed a steady increase in 2008 to 2010, then dropped slightly in 2011, before peaking at 2.36 in 2013, then fell back to the trough in 2014, and again going back to an increasing trend in 2015. Overall, the IRR of AMI HAs in Khanh Hoa increased significantly by 10% (IRR 1.10, 95% CI 1.08–1.13, p < 0.001) per year from 2008-2015. The AMI HAs in Thua Thien Hue had their first peak in 2008, then faced a trough in 2008-2009, followed by a slight and steady increase and peak in 2013, lastly dropping for the last two years. The IRR of AMI HAs in Thua Thien Hue from 2008-2015 showed a significant increase of 5% per year (IRR 1.05, 95% CI 1.02–1.07, p < 0.01). On the other hand, AMI HAs in Quang Binh peaked in 2012, then lowered in 2013, and increased again in 2014 and 2015. Quang Binh experienced a significantly increasing trend for the studied period, with a 12% increase per year (IRR 1.12, 95% CI: 0.10–1.17, p < 0.001).
100 Chapter 5: Analysis of Seasonality in Hospital Presentations for Acute Myocardial Infarction in Central Vietnam
AMI hospital admissions by provinces
2.5
2.0
1.5
1.0
0.5 Rates per 100,000 population 100,000per Rates
0.0 2008 2009 2010 2011 2012 2013 2014 2015 Year TTH KH QB
Figure 5.1 Rates for hospital admissions (Jan 2008-Dec 2015) of AMI patients by provinces
5.3 SEASONALITY OF HOSPITAL ADMISSIONS DUE TO ACUTE MYOCARDIAL INFARCTION
Visually, the HAs of AMI patients reveal a winter peak and low points in summer (Figure 5.2), and that seasonal variations of AMI HAs varied by year (Figure 5.3).
Chapter 5: Analysis of Seasonality in Hospital Presentations for Acute Myocardial Infarction in Central Vietnam 101
Figure 5.2 Numbers of AMI HAs by seasons and provinces
102 Chapter 5: Analysis of Seasonality in Hospital Presentations for Acute Myocardial Infarction in Central Vietnam
Figure 5.3 Numbers of AMI HAs by seasons and year
The results of the regression for seasonal data for each province are summarised in Table 5.1. There were significant differences between AMI HA counts in winter compared to summer (p < 0.01) in all three studied provinces. The incidence of AMI was higher in winter than summer; however, the effect of winter on AMI HAs gradually reduced from the north to the south. In Quang Binh (in the north), the incidence was 49% (IRR 1.49, 95% CI: 1.24-1.78) higher in winter than in summer, while the incidence of AMI in Thua Thien Hue and Khanh Hoa (moving to the south) were 37% (IRR 1.37, 95% CI: 1.20–1.56) and 24% (IRR 1.24, 95% CI: 1.10–1.41) higher in winter than in summer.
Table 5.1 Summary of seasonal comparison of AMI HAs by provinces Season Khanh Hoa Thua Thien Hue Quang Binh IRR (95%CI) IRR (95%CI) IRR (95%CI) Summer 1.00 1.00 1.00 Spring 1.05 (0.92-1.20) 1.20 (1.04-1.37)* 1.20 (1.00-1.45) Fall 1.10 (0.97-1.25) 1.23 (1.07-1.40)* 1.34 (1.12-1.61)** Winter 1.24 (1.10-1.41)** 1.37 (1.20-1.56)*** 1.49 (1.24-1.78)*** Note: ‘<0.1, *<0.05, **<0.01
As the results could be influenced by different definitions of a season, the data were also analysed following the season definition of the northern hemisphere climatic seasons based on the official “calendar” definitions: winter (December 21 to March 19 [89 days]), spring (March 20 to June 20 [93 days]), summer (June 21 to September 22 [94 days]), fall (September 23 to December 20 [89 days]) (Spencer et al., 1998). The length of each season was also adjusted to 91 days to take into account the difference in the length of each season (Ornato et al., 1996). However, the conclusions were similar to the previous definition for seasons.
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5.4 SEASONALITY IN HOSPITAL ADMISSIONS DUE TO ACUTE MYOCARDIAL INFARCTION IN DIFFERENT GENDER AND AGE GROUPS
Table 5.2 provides the comparisons of the seasonality of AMI HAs among different gender and age groups. The older age group had a significantly higher rate of AMI HAs during winter in all three studied provinces, while there was a different seasonal effect of AMI HAs on male and female groups in different locations. In Khanh Hoa, in the South Central Coast region, the male group had a significantly higher risk of AMI HAs during winter (than during summer) (IRR = 1.28, 95%CI 1.07–1.52). In contrast, females had a significantly higher risk of AMI HAs during winter (than during summer) in Thua Thien Hue and Quang Binh (IRR = 1.64 (1.33–2.03) and 1.68 (1.20–2.37), respectively). Among these significant effects within each subgroup, the effect of winter on AMI HAs also gradually reduced from the north to the south. For instance, the risk of AMI HAs for the elderly group in winter compared to summer was 1.54 (95%CI: 1.24–1.93) in Quang Binh, 1.41 (95%CI: 1.21–1.65) Thua Thien Hue, and 1.25 (95%CI: 1.07–1.46) in Khanh Hoa.
104 Chapter 5: Analysis of Seasonality in Hospital Presentations for Acute Myocardial Infarction in Central Vietnam
Table 5.2 Summary of seasonal comparison of AMI HAs by gender and age groups
Provinces Summer Spring Fall Winter (south to north) (Reference) IRR (95% CI) IRR (95% CI) IRR (95% CI) Khanh Hoa Male 1.00 1.01 (0.84–1.21) 1.03 (0.86–1.24) 1.28 (1.07–1.52)* Female 1.00 1.15 (0.92–1.42) 1.21 (0.98–1.50) 1.19 (0.96–1.47) Age < 65 1.00 1.18 (0.96–1.46) 1.11 (0.89–1.38) 1.18 (0.96–1.46) Age ≥ 65 1.00 0.97 (0.83–1.15) 1.07 (0.91–1.25) 1.25 (1.07–1.46)* Thua Thien Hue Male 1.00 1.14 (0.96–1.35) 1.02 (0.86–1.22) 1.19 (1.00–1.41)’ Female 1.00 1.33 (1.07–1.66)* 1.55 (1.25–1.92)*** 1.64 (1.33–2.03)*** Age < 65 1.00 1.27 (0.98–1.64) 1.17 (0.91–1.52) 1.24 (0.96–1.60) Age ≥ 65 1.00 1.17 (0.99–1.37) 1.24 (1.05–1.45)* 1.41 (1.21–1.65)*** Quang Binh Male 1.00 1.21 (0.98–1.51) 1.28 (1.04–1.58)’ 1.36 (1.10–1.67)* Female 1.00 1.14 (0.78–1.65) 1.32 (0.93–1.90) 1.68 (1.20–2.37)* Age < 65 1.00 1.14 (0.81–1.62) 1.64 (1.19–2.28)* 1.24 (0.88–1.75) Age ≥ 65 1.00 1.23 (0.98–1.55) 1.13 (0.90–1.43) 1.54 (1.24–1.93)** Note: ‘<0.1, *<0.05, **<0.01; IRR: Incident rate ratio
5.5 DISCUSSION
Long-term trends of hospital admissions due to acute myocardial infarction in the Central Coast region of Vietnam
While the incidence of AMI has decreased or plateaued in many countries (Armas et al., 2012; Jennings, Bennett, Lonergan, & Shelley, 2012; Koopman et al., 2013; Schmidt, Jacobsen, Lash, Bøtker, & Sørensen, 2012; Spatz, Beckman, Wang, Desai, & Krumholz, 2016), the disease continues to increase in Vietnam and is one of the leading causes of mortality and morbidity (World Health Organization, 2014b; World Health Organization 2017c). In the current study, the trend of AMI HAs varied across different provinces for the eight-year period from 2008-2015. Thua Thien Hue, where the first cardiovascular intervention unit was established and developed in Central Vietnam in 2003, had the lowest rate of AMI HAs increase, with roughly 5% (2%–7%, p = 0.0019) per year. An advanced cardiovascular intervention unit was established in Khanh Hoa in 2009, and in this province, the estimated increase of AMI HAs per year was around 10% (7%–13%, p < 0.001). Whereas, Quang Binh, which did not have a specialised cardiovascular intervention unit until 2016, had the highest rate of AMI HAs, with an increase of approximately 13% (10%–17%, p < 0.001) per year during the period of 2008 to 2015. The highest rate of AMI HAs in 2008 was in Thua Thien
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Hue (roughly double that of Quang Binh, and one and a half that of Khanh Hoa); however, by the end of the studied period, all three provinces had approximately the same IRR.
Apart from overall increasing trends, all provinces experienced their peak (in Khanh Hoa and Thua Thien Hue) or trough (in Quang Binh) in 2013. This may only be a statistical anomaly due to differences in environmental or socio-economical characteristics of each province, or due to changes within the hospitals. Several main demographic and socio-economic development characteristics of these three provinces are summarised in Appendix G. There appears to have been no major weather events in 2013, such as El Nino, La Nina, or an unusual number of weather disadvantage events, such as heat waves or cold spells (NCHMF (Vietnamese National Center for Hydro-Meteotological Forcasting), 2014; US Climate Prediction Center, 2015).
The most likely reason for the trend variations is the development of a cardiovascular intervention department at the hospitals and changes in diagnosis/detection. Hue Central Hospital (Thua Thien Hue) has a long and well-developed scheme for specialised cardiovascular intervention, becoming the leading centre in Central Vietnam to date. Khanh Hoa General Hospital developed its cardiovascular intervention department later than Hue Central Hospital, beginning to receive cardiovascular intervention techniques from Cho Ray Hospital (Ho Chi Minh City) from 2009 and had a remarkable development in 2013 through the establishment of the Thoracic Surgery department and was classified as a satellite hospital of Cho Ray Hospital for cardiovascular intervention. These facts may explain the surge of AMI incidence in Khanh Hoa General Hospital in 2013, when a number of AMI cases transferred from other two smaller provincial hospitals in Cam Ranh and Ninh Hoa of Khanh Hoa province for PCI treatment or attracted other AMI patients, those who intended to have treatment in other specialised hospital in Ho Chi Minh City to Khanh Hoa General Hospital. In addition, during informal interviews with doctors at these hospitals they suggested that the introduction of the third universal definition for AMI in late 2012 and utilisation of a high sensitive Troponin (a highly sensitive biomarker for AMI detection in 2013) may have also partly contributed to the surge in AMI incidence at these two hospitals in that year.
Unlike the other two hospitals, Vietnam-Cuba Friendship Hospitals (Quang Binh) only recently established its cardiovascular intervention department in 2016. As such, all AMI patients hospitalised from 2008-2015 did not receive percutaneous coronary intervention, an important treatment for AMI at the hospitals. This may have depleted the number of AMI cases presenting at Quang Binh, as patients may have bypassed this centre and been transported
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to the Hue Central Hospital or the National Heart Institutes in Hanoi. During the eight-year period, 145 out of 712 AMI cases (20.4%) from Quang Binh were found to be hospitalised in Hue Central Hospital rather than the local Vietnam-Cuba Friendships Hospital. There was no information regarding the number of cases from Quang Binh that went to other non-studied centres for treatment, such as those in Hanoi. However, doctors at the Vietnam-Cuba Friendships Hospital reported their belief that, in 2013, a large number of AMI patients went to other hospitals for treatment due to the active promotion of other cardiovascular intervention centres, including those in Hue and Hanoi. This could help to explain the trough of AMI incidence in Quang Binh in this year.
Seasonality of hospital admissions due to acute myocardial infarction
Identification of the specific seasonality of AMI occurrence is of scientific importance because such patterns imply that there are triggers external to the atherosclerotic plaque. The seasonal pattern observed for AMI over a year has been reported for decades. Interestingly, while a majority of the literature has demonstrated a winter peak for AMI incidence or death (Abrignani et al., 2009; Khan & Halder, 2014; J. Yang et al., 2017), no significant patterns of seasons were also reported (Fernández-García et al., 2015; Ravljen et al., 2018). For studies of winter peak, the cold climate seems to be an important factor in precipitating AMI in the current study.
The current study also indicated that there was an overall significantly higher number of AMI HAs in all provinces in winter than in summer (a winter effect). The risk of AMI HAs were 24% (95%CI, 10%–41%), 37% (95%CI, 20%–56%), and 49% (95%CI, 24%–78%) higher in Khanh Hoa, Thua Thien Hue, and Quang Binh, respectively. However, when further analysis was conducted among different subgroups of gender and age, the results suggested two different conclusions about seasonality in two different climate zones. In Khanh Hoa, which is in the South Central Coast region (a tropical savanna zone), the winter effects only appeared in males. On the other hand, in Thua Thien Hue and Quang Binh, in the North Central Coast region (a tropical monsoon zone,) the results still revealed a winter effect in various subgroups, especially in females and the elderly (aged 65+). A particular result worth noting is that the winter effect of AMI HAs appeared to fade from the north to the south within each subgroup, for example, the winter effects significantly reduced on AMI HAs from Quang Binh to Thua Thien Hue and Khanh Hoa in the elderly, as the IRRs were 1.54 (1.24–1.93), 1.41 (95%CI: 1.21–1.65), and 1.25 (95%CI: 1.07–1.46), respectively. Furthermore, winter effects were not significant in younger groups (aged less than 65) for all three provinces (p > 0.05).
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Explanation for the seasonal variation for health outcomes mostly depends on environmental temperature or other factors (Kwon et al., 2015; Mohammadi et al., 2018; Shuie et al., 2016; Yamaji et al., 2017). Environmental factors, as well as human behaviour or adaption changing could be the trigger for AMI occurrence, for example, influenza circulation, change in diet and physical activities, stress etc. It would seem that the risk of HAs for AMI can depend more on cold and hot temperature or influenza circulation, as an increasing number of studies have reported the significant increase of the incidence with ambient temperature variations (Kwon et al., 2015; Mohammadi et al., 2018; Shuie et al., 2016; Yamaji et al., 2017).
Figure 5.4 shows the monthly distribution of the mean temperatures in the studied provinces. In Khanh Hoa, there is no extremely cold weather during the winter period in the tropical savanna climate. The temperature rarely drops below 17oC (compared with 11oC in Thua Thien Hue and 10oC in Quang Binh). The region is not very hot in the summer, with the temperature not usually higher than 37oC (compared to 40oC in Thua Thien Hue and 41oC in Quang Binh) (National Hydro-meteorological and Environment Network Center, 2016). This might indicate that locations where the temperature range is narrower may experience less of a winter effect on AMI HAs. Another explanation for the higher rate of AMI incidence in winter relates to influenza circulation. There is consistent ecologic evidence that acute respiratory infection—influenza in particular—is a possible trigger for AMI (Finelli & Chaves, 2011; Warren-Gash et al., 2011; Warren-Gash et al., 2009). It has also been suggested that inflammation plays a decisive role in the pathophysiology of AMI (Finelli & Chaves, 2011). The uncertainties regarding the role of temperature and influenza circulation in the seasonality of AMI HAs revealed in the results of the current study require further examination or analysis with regards to the effect of temperature and influenza on AMI HAs.
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Figure 5.4 Monthly distributions of temperature ranges in the studied provinces
Current understanding of the possible mechanisms for the winter effect on AMI HAs suggests a complex relationship between seasons and pathophysiological exogenous and endogenous individual factors (Abrignani et al., 2008). Cold weather or sudden changes in the climate can increase arterial blood pressure, arterial spasm, platelet and red blood cell counts, blood viscosity, plasma fibrinogen, and factor VII and serum cholesterol levels. Exposure to cold also has important hemodynamic effects, including an increase in systemic vascular resistance, myocardial oxygen consumption, and body metabolism (Finelli & Chaves, 2011; Warren-Gash et al., 2011; Warren-Gash et al., 2009). Concurrent infections during cold weather winter months, particularly those involving the respiratory tract, have also been postulated to be a trigger for AMI morbid events. Other mechanism that have been proposed to explain the rise in cardiovascular events during cold weather include seasonal change in physical activity, diet, weight, stress during the holiday season, and seasonal modulation in the secretion of physiologically active substances analogous to those that trigger seasonal depression (Claeys et al., 2017; Long et al., 2018).
An increase of four to six AMI HAs cases per month for the peak season in the current study regions did not appear to be a remarkable burden for the related cardiovascular intervention units. However, an improved understanding of the seasonality of HAs and which
Chapter 5: Analysis of Seasonality in Hospital Presentations for Acute Myocardial Infarction in Central Vietnam 109
seasonally recurring factors drive or influence health service use could be useful in hospital management for improved service planning and providing care for other places with higher risk but limits in their resources. A “winter bed crisis for AMI” is a common feature of public hospitals. A reallocation of resources in terms of increasing staff and hospital beds could be undertaken in winter, as compared to summer, to meet the expected increase in demand for AMI patients. Potentially preventing excess admissions is a further goal; however, this requires more understanding of the seasonal mechanism. Targeted preventative policies that result in a reduction in HAs could achieve major cost savings for hospitals and health departments, and reduce the burden of disease among this vulnerable group (D’Souza et al., 2007).
Strengths and limitations
This study has three key strengths. To the best of my knowledge, this is the first multicity study to quantify seasonal variations of AMI incidence in Vietnam. Secondly, data were collected directly from hard-copy medical records of AMI stored in the hospitals and the patient’s diagnosis was double-checked; thus, the quality of the data is much better in terms of completeness and accuracy than studies that use only electronic databases (Pham et al., 2014; Phung, Guo, Thai, et al., 2016). Furthermore, advanced statistical approaches were used, which allowed for much needed flexibility to examine the seasonal effects on AMI.
The present study also has some limitations. First, morbidity studies based on HAs explore only selected events in patients surviving long enough to be hospitalised, and therefore it does not include many fatal cases of AMI. Therefore, the trends and seasonality of AMI occurrence may have been underestimated in the studied regions. Moreover, this study was a centre-based observational analysis, where one highest-level hospital was selected for each province, and may not reflect the complete scenario of the province in particular, or of Central Coast of Vietnam in general. Lower level hospitals may have lesser times from the onset of AMI occurrence to the hospitalisation than we found in the provincial level hospitals (where time for transferring patients from low-level hospitals to the high-level hospital for the reperfusion treatment of AMI should be considered). The selection bias might lead to an underestimation of the associations. This issue was discussed in detail in Chapter 4.
Despite the limitations of the study, significant seasonal variation was found in HAs due to AMI appearing to vary by region/province, these results could be used to improve prevention measures, therapeutic management, and educational strategies. For example, healthcare systems should adjust the availability of emergency services and other hospital resources to the
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most vulnerable periods and regions. Susceptible patients should be informed of the higher risk during winter, and the demonstration of a higher risk period could be useful for general practitioners to improve causative prevention measures, therapeutic management, and educational strategies.
5.6 CONCLUSION
Overall, the results showed increasing trends, as well as a winter peak of AMI HAs in Central Coast of Vietnam, with the increased rate for Khanh Hoa, Thua Thien Hue, and Quang Binh being 10% (7%–13%), 5% (2%–7%), and 12% (10%–17%) per year during 2008–2015. The incidence of AMI peaked significantly in winter compared to summer, risks of AMI HAs were 24% (10%–41%), 37% (20%–56%) and 43% (24%–78%) higher in Khanh Hoa, Thua Thien Hue, and Quang Binh, respectively. In terms of the risk to different age groups, the group aged 65+ had a significantly higher rate of AMI HAs during winter across the three provinces, males were more likely to be at risk of AMI HAs during winter in Khanh Hoa (South Central Vietnam), while females were more likely to be at risk in Thua Thien Hue and Quang Binh (North Central Vietnam). Moreover, the winter effect of AMI admissions gradually reduced from the north to the south. The findings of this study could therefore be used to improve prevention measures, therapeutic management, and educational strategies.
The following chapter examines the association of the short-term effects of ambient temperature on the risk of AMI HAs in Central Coast of Vietnam.
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Chapter 6: Short-term Effects of Temperature on Hospital Admissions of Acute Myocardial Infarction in Central Vietnam
With the aim of examining the short-term effects of ambient temperature on hospital admissions (HAs) for acute myocardial infarction (AMI) from different climate zones, data from Quang Binh and Thua Thien Hue have been pooled for analysis in this chapter. These results are referred to as the North Central Coast region (tropical monsoon climate zone), results from Khanh Hoa province are referred to as the South Central Coast region (tropical savanna climate zone).
6.1 INTRODUCTION
Although meteorological factors are not implicated as a direct cause of the pathology that leads to AMI, they could exacerbate the underlying condition and precipitate the onset of a heart attack. Cold temperatures may precipitate myocardial ischaemia and coronary plaque instability due to various simultaneous processes, such as stimulating cold receptors in the skin (leading to a rise of blood catecholamine), increasing diuresis, eliciting pulmonary neurogenic, and exacerbating pre-existing pulmonary conditions (Claeys et al., 2017). Moreover, hot temperatures could lead to cardiac output rises due to vasodilation of blood vessels, inadequate thermoregulation, and elevated plasma viscosity and serum cholesterol levels, which may increase the risk of AMI admission (Mohammadi et al., 2018). Nevertheless, there are inconsistent conclusions regarding the impact of adverse temperatures on AMI admissions. While there is evidence that high temperatures increased hospital visits and admissions for AMI in several cities in Australia and Italy (M. Loughnan et al., 2014; Morabito et al., 2006), many studies have only found a significant increase in AMI admissions at low temperatures (Bijelović et al., 2017; Honda et al., 2016; Ravljen et al., 2018; Tian et al., 2016). A number of studies have suggested a significant adverse impact of both high and low temperatures on AMI incidence (Kwon et al., 2015; Mohammadi et al., 2018; Yamaji et al., 2017); other studies in Spain and the Netherlands have shown no significant association between ambient temperatures and AMI admissions (Fernández-García et al., 2015; Wijnbergen et al., 2012). Most previous studies have been conducted in developed countries with temperate climatic
112 Chapter 6: Short-term Effects of Temperature on Hospital Admissions of Acute Myocardial Infarction in Central Vietnam
zones. To date, few studies have examined the association between temperature and admissions for AMI in developing countries with tropical climates (Bhaskaran et al., 2009b).
Vietnam is a developing low-income country that is highly vulnerable to climate change (Rubin, 2014; A. Yusuf & Francisco, 2009). The country has a long body, stretching from the latitudes 8° and 23°N, encompassing various climate zones. The Central Coast of Vietnam has two separated climate zones: tropical monsoon climate in the north and tropical savanna climate in the south. The Central Coast region is vulnerable to adverse weather conditions and natural disasters (General Department of Preventive Medicine, National Institute of Hygiene and Epidermiology, World Health Organization, & Oxford University Clinical Research, 2014a, 2014b). Every year the region has the highest frequency of typhoons, droughts, floods, hot winds from Laos, and cold north-easterly winds in the winter compared to other areas of the country (People's Committee of Quang Binh Province 2013; People's Committee of Thua Thien Hue Province 2013).
Concurrently, Vietnam is undergoing an epidemiological transition, in which overall mortality and morbidity patterns have shifted from communicable to non-communicable diseases (L. Nguyen, Nguyen, et al., 2014; World Health Organization, 2018). Recent national surveys (STEPS 2010 and STEPS 2015 on non-communicable disease risk factors) have shown that the prevalence of risk factors for cardiovascular diseases (CVDs) in general and AMI in particular were high and most showed increasing trends as partly showed in the results in Chapter 5 (Department of Preventive Medicine-Vietnamese Ministry of Health, 2016). Compared to 2010, in the 24-64 age group, the prevalence of current alcohol consumption in 2015 was higher (44.8% vs 37.0%), and similar to that of overweight people (17.5% vs 12%). Moreover, 22.5% of the population smoked, this prevalence in male and female were 45.3% and 1.1% in 2015. (Department of preventive medicine-Vietnamese Ministry of Health, 2016). More detailed results about STEPS 2015 and STEPS 2010 are presented in Appendix F.
A recent report of the World Health Organization has indicated that one-third of total deaths due to non-communicable diseases were attributed to CVDs, mainly strokes and ischaemic heart diseases (IHDs), and CVDs are ranked first among the causes of mortality in Vietnam. These diseases have also been found to make up the largest share (approximately 20%) of the total burden of disability-adjusted life years (DALYs) lost (World Health Organization 2017c). Even though the burden of CVDs in the Central Coast region is lower than for other regions of the country, the region follows a similar increasing trend. AMI sits at
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the 7th highest cause of death in the Central Coast region with 0.45 deaths per 100,000 in 2015 (Ministry of Health 2017).
This study examines the relationships between temperature and AMI in Vietnam, particularly in the Central Coast region. The focus is on the short-term effects of ambient temperature on daily HAs due to AMI.
Time-series regression methods were applied to examine the association between AMI admissions and temperature using generalised linear model (GLMs) and a distributed lag non- linear model (DLNM) (Gasparrini et al., 2010a), taking into account over-dispersion by using a negative binomial distribution. A 24-hour mean was used as a temperature measure, which is consistent with other studies in this area (Loughnan et al., 2010b; Ravljen et al., 2018; Wolf et al., 2009). The effect of ambient temperature on IRR of AMI HAs was estimated, adjusting for long-term trends, seasonality, weekend and holiday, influenza-like illness incidence, and other meteorological factors. The modelling was conducted in two stages to examine the impact of temperature separately from the influence of long-term trends, seasonality, and other influences. Further details are provided in Section 3.6.4.
Subgroup analyses were also performed to evaluate whether there were any modification effects caused by patient characteristics (i.e., gender, age, and disease status). The influence of different degrees of freedom for the splines, cross-basis of DLNM and of other meteorological variables, the maximum lag, exclusion of data for 2013, a one-stage model, the use of an alternative approach called case-crossover analysis were assessed for the sensitivity analysis, and due to the low number of daily admissions, a zero-inflated Poisson model was also applied. The sensitivity analyses were used to check the robustness of the results. Further details about these approaches were outlined in Sections 3.6.4 and 3.6.6 of Chapter 3.
6.2 DESCRIPTIVE TIME-SERIES CHARACTERISTICS OF HOSPITAL ADMISSIONS DUE TO ACUTE MYOCARDIAL INFARCTION
Summary statistics for the AMI HAs and meteorological variables are shown in Tables 6.1 and 6.2. Moreover, Figures 6.1 and 6.2 present the time-series distribution of monthly AMI cases and mean temperature because the daily numbers of AMI HAs were low with medians were 0 and ranges from 0 to 5 cases per day.
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Table 6.1 Descriptive statistics of monthly AMI hospital admissions by age, gender, and health status in the South Central Coast region and the North Central Coast region, 2008-2015 No. of daily HAs The South Central Coast The North Central Coast n mean median (range) n mean median (range) (SD) (SD) Total 1342 14.0 (6.2) 13.7 (2.0 – 35.8) 1986 20.7 (6.4) 19.9 (9.1 – 37.3) By sex Male 840 8.8 (4.3) 8.8 ( 1.0 – 21.7) 1264 13.2 (4.4) 12.7 (4.9 – 27.5) Female 502 5.2 (3.2) 4.9 (0.0 – 14.2) 722 7.5 (3.4) 7.1 (2.0 – 15.7) By age group 18 - 64 492 5.0 (2.6) 4.8 (0.0 – 12.0) 562 5.8 (2.9) 4.9 (1.0 – 14.5) ≥ 65 850 8.7 (4.7) 8.7 (0.0 – 23.2) 1424 14.5 (5.1) 14.5 (3.9 – 27.1) STEMIa status STEMI 721 7.5 (3.5) 7.1 (0.0 – 16.3) 1262 13.1 (4.7) 13.4 (4.9 – 25.5) Non-STEMI 621 6.5 (4.3) 5.9 (0.0 – 19.5) 652 6.8 (3.3) 6.0 (1.0 – 15.7) Comorbidity status Yes 947 9.9 (5.2) 9.1 (1.0 – 27.1) 1147 11.9 (4.7) 11.8 (2.0 – 23.5) No 395 4.1 (2.6) 3.9 (0.0 – 12.7) 767 8.0 (3.1) 7.8 (1.0 – 14.7) Pre-cardiovascular diseases Yes 875 9.0 (4.6) 8.3 (1.0 – 22.0) 1181 12.1 (4.8) 11.6 (3.9 – 24.2) No 467 4.8 (2.6) 4.8 (0.0 – 11.6) 733 7.5 (3.0) 7.0 (1.9 – 15.0)
Table 6.2 Distribution of selected meteorological and influenza-like illness variables in the South Central Coast region and the North Central Coast region, 2008-2015 Variables South Central Coast North Central Coast Mean (range, Mean (range, 90th percentile) 90th percentile) Daily mean temperature (oC) 27.1 (23.4–29.9) 25.1 (16.8–31.2) Daily minimum temperature(oC) 24.7 (21.1–27.4) 22.3 (15.0–27.2) Daily maximum temperature (oC) 30.3 (25.9–33.8) 29.1 (19.0–36.9) Daily relative humidity (%) 78.4 (70–87) 84.5 (69–94) Daily air pressure (mmb) 1009 (1003.6–1014.8) 1009 (1001.3–1019.4) Daily wind velocity (m/s) 2.7 (1.0–6.0) 1.6 (1.0–3.0) Influenza-like illness count per 59.4 (39–90) 109.5 (77–169) month
Chapter 6: Short-term Effects of Temperature on Hospital Admissions of Acute Myocardial Infarction in Central Vietnam 115
Figure 6.1: Seasonal distribution of monthly AMI HAs and temperature in the South Central Coast region, 2008–2015
Note: The red vertical lines represent the summer period of each year, including June, July, and August
Figure 6.2 Seasonal distribution of monthly AMI HAs and temperature in the North Central Coast region, 2008–2015
Note: The red vertical lines represent the summer period of each year, including June, July, and August
116 Chapter 6: Short-term Effects of Temperature on Hospital Admissions of Acute Myocardial Infarction in Central Vietnam
The Spearman’s correlation coefficients among the AMI HAs, meteorological factors and influenza like illness are presented in Table 3. Overall, daily AMI HAs found to have no or very weak monotonic associations with the meteorological measures as well as influenza like illness. The results also showed that ambient temperature had negative monotonic correlations with other meteorological factors (i.e. relative humidity, air pressure, wind speed, p < 0.01) in both regions. Besides, influenza like illness was weakly correlated with those meteorological factors. Details of the precise nature of the studied correlations are presented in Appendix H.
Table 6.3 Spearman coefficient of AMI, meteorological factor and influenza-like illness in the South Central Coast region and the North Central Coast region, 2008–2015
Note: *<0.05, **<0.01, ***<0.001. AMI HAs: daily AMI admissions; MT: Mean temperature; RH: Relative humidity; AP: Air pressure; WS: Wind speed; Inf: daily influenza like illness counts
6.3 HEAT EFFECT IN THE SOUTH CENTRAL COAST REGION
An overall picture of the effect of temperature on AMI HAs is provided in Figure 6.3, showing the relative risks along temperature and lags compared with a reference value of 23.4 °C (corresponding to the threshold temperature) in the South Central Coast region. The plot shows a very strong and immediate effect of temperature and reveals a delayed effect for high temperature in this region. Additionally, inspection of the plot suggests some harvesting effects for high temperature, which means that after an increase in relative risk following exposure, there was a period of time when the relative risk was around 1 (suggesting no association). The plot also shows a second surge of relative risk at very high temperature at long delay times. However, this plot does not present the confidence interval range; thus, there is some uncertainty in drawing any conclusions from just this single plot.
In order to clearly reflect the relationship between temperature and AMI, the cumulative effects were plotted at a 21 day lag, which indicated a positive slope with
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increasing temperature. The relative risk of hospitalisation due to AMI was 18% (RR: 1.18, 95% CI: 0.95–1.47, p > 0.05) higher at moderately high temperatures (90th percentile of temperature range – 29.5oC) than the reference temperature (23.4oC). Moreover, this RR was 36% (RR: 1.36, 95% CI: 1.06–1.73, p < 0.05) higher at extreme high temperature (95th percentile of temperature range – 29.9oC) than the reference temperature. The results of various sensitivity analyses are reported in Appendix K.
Regarding defining the reference temperature using DLNM, the threshold points where the rate of AMI HAs were lowest in the multi-variable model was chosen, in which temperature corresponding to the minimum estimated relative risk of within the 5th to 95th percentile of temperature range was selected. For the South Central Coast region, the reference temperature selected was 23.4oC; however, it is worth noting that the second change point for heat effect for the South Central Coast region was 28.6oC (Figure 6.4). This figure could be used as a temperature threshold for warning local residents about the elevated risk of AMI occurrence when the ambient temperature exceeds this value. This is because, after this second change point, the risk of AMI HAs increased steadily. Further details about the reference temperature selection are presented in Section 6.4.
Figure 6.3 Three-dimensional graph showing the relative risk along temperature range and lags, with reference temperature at 23.4 °C for the South Central Coast (Khanh Hoa) region
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Figure 6.4 The overall effect (lag 0-21) of temperature on hospital admissions due to AMI in the South Central Coast region, Vietnam in 2008-2015 (red line), with 95% confidence intervals (grey area)
The overall effect of temperature-AMI was also examined in reference to different subgroups (gender, age group and health status). The exposure-response curves showed there is no significant visual difference in the temperature-AMI relationship pattern between the subgroups (Figure 6.5). Although the plots showed different levels of the exposure-response, there appears to be a statistically significant increase in the elderly, Non-STEMI and those having at least one comorbidity.
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Figure 6.5 The overall effects (lag 0-21) of temperature for all subgroups on hospital admissions due to AMI in the South Central Coast region, Vietnam in 2008-2015 (red line), with 95% confidence intervals (grey area)
Notes: Pre-CVDs: Having history of pre cardiovascular diseases; STEMI: ST-segment elevation myocardial infarction; Vertical axes are relative risk (with scales at 0 -1–2–3–4–5) and horizontal axes are mean temperature (with scales at 22–24–26–28–30–32)
Figure 6.6 (the left graphs from top) illustrates the RR by lag at selected temperatures. From these graphs, it appears that the effects of high temperature varies by lag time although there is much uncertainty. At an extremely high temperature (99th percentage) (plot in the bottom left), the plot shows a possibly strong and immediate effect on AMI HAs (at lag 0 with about 3.4% increase of RR, p = 0.856). Moreover, the elevated risk lasted within 7 days. However, the uncertainty for this effect is high because there was only 16 AMI cases hospitalized during 31 days that had ambient temperature equal to or higher than that temperature threshold.
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Figure 6.6 Relative risk (RR) of AMI admissions by temperature and lag using cross-basis smoothing Note: Left from top: relative risk by lag at selected temperatures 25.7oC, 27.4oC, 28.7oC, and 30.7oC. Right from top: relative risk by temperature at selected lags 0, 3, 7, and 14. Risk of AMI admission is scaled to be relative to that at 23.4oC (reference temperature). The 95% confidence intervals are reported as shaded areas.
6.4 COLD EFFECT IN THE NORTH CENTRAL COAST REGION
The results from the North Central Coast region showed almost the opposite pattern compared to the South Central Coast region, with cold temperatures more likely to induce an adverse impact than hot temperatures. A 3D plot (Figure 6.7) outlines the overall pattern of the effect of temperature on AMI HAs. This plot shows a strong and immediate effect of very high temperatures, while there was also a delayed
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but strong effect of low temperatures in this region. However, this figure does not present the confidence interval of the estimates. Figure 6.8, which plots the cumulative effects of 21 days lag and presents the estimates with a 95% confidence interval, indicates that there was a significant increase of risk of AMI admissions when the ambient temperature decreased. In particular, the relative risk of hospitalisation due to AMI was higher 11% (RR: 1.11, 95% CI: 0.91–1.35, p > 0.05) at a moderate low temperature (10th percentile of temperature range–18.5oC) than the reference temperatures (21.2oC). Moreover, the relative risk of hospitalisation due to AMI was 25% (cumulative RR: 1.25, 95% CI: 1.02–1.55, p < 0.05) higher at extreme low temperatures (5th percentile of temperature range–16.8oC) than the reference temperature. The reference temperature (21.2oC) for the North Central Coast region was selected by applying a similar approach to that used for the South Central Coast, as discussed previously. More details about this calculation are presented in Section 6.4.
Figure 6.7 Three-dimensional graph of the relative risk along temperature range and lags, with a reference temperature at 21.2 °C for the North Central Coast (Thua Thien Hue and Quang Binh) region
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Figure 6.8 Overall effects (lag 0-21) of temperature on hospital admissions due to AMI in the North Central Coast region in 2008-2015 (red lines), with 95% confidence intervals (grey areas)
Figure 6.9 The overall effects (lag 0-21) of temperature for all subgroups on hospital admissions due to AMI in the North Central Coast region, Vietnam in 2008-2015 (red line), with 95% confidence intervals (grey area)
Notes: Pre–CVDs: Having history of pre-cardiovascular diseases; STEMI: ST-segment elevation myocardial infarction; Vertical axes are relative risk (with scales at 0–1–2–3–4–5) and horizontal axes are mean temperature (with scales at 15–20–25–30).
Figure 6.9 depicts the RR by lag at selected temperatures 14.6oC, 22.2oC, 25.8oC, and 28.6oC, standing for 1st , 25th , 50th and 75th percentiles of ambient temperature in the North Central Coast region (left column of figures). A cold effect was found to
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generally occur 3-16 days following exposure at the 1st temperature range (graph in the top left), showing delayed effects of temperature on AMI HAs at low temperature thresholds. Also presented, is the RR by lag at different lag 0, 7, 14 and 21 days (right column of figures). Low temperatures appear to increase the risk of AMI HAs after one week and two week, graph for lag 7 and lag 14 (the second and third graphs from the top in the right side) shows significant elevated risk when the temperature decreases from around 20oC. Graphs at Lag 0 and Lag 21 do not appear to show an effect.
Figure 6.10 Relative risk (RR) of AMI admissions by temperature and lag using cross-basis smoothing
Note: Left from top: relative risk by lag at selected temperatures 14.6oC, 22.2oC, 25.8oC, and 28.6oC. Right from top: relative risk by temperature at selected lags 0, 7, 14 and 21. Risk of AMI admission is scaled to be relative to that at 21.2oC (reference temperature). The 95% confidence intervals are reported as shaded areas.
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Due to the unusually high number of AMI admissions in 2013 (see Chapter 5), the results were also analysed excluding the data for that year; however, the analysis without the outlier year showed no significant difference from the findings for the whole eight-year period. In addition, various models were applied in the sensitivity analysis, all the results showed the similar negative exposure-response effects but some of them were not statistically significant. The results of various sensitivity analyses are reported in Appendix L.
6.5 DISCUSSION
In this study, the influence of temperature on HAs for AMI at the daily level was assessed in two separate areas of the Central Coast of Vietnam and yielded several notable findings. The temperature effect on AMI admission appeared to differ in the two regions. In the South Central Coast region (savanna tropical climate zone), there was some evidence of a heat effect, as the risk of AMI HAs increased when the temperature was relatively high. In contrast, a cold effect was observed in the North Central Coast region (monsoon tropical climate zone) where the risk increased when the temperature was relatively low. In addition, in relation to subgroups analysed, it is likely that there were those with the elderly, Non-STEMI groups, or having at least one comorbidity vulnerable to heat, while the detrimental impact of cold temperature was significantly higher in male patients and the elderly.
This is the first study to examine the association between temperature and HAs due to AMI in Vietnam. A previous study found a significantly positive relationship between cold weather and CHA admissions in a city in northern Vietnam (Pham et al., 2014), while another study investigated the relationship between elevated temperature and CHA admissions in Ho Chi Minh City in Southern Vietnam (Phung et al., 2016). Other studies have explored the association between temperature and all-cause mortality or focused on the relationship between meteorological factors and water- and vector-borne diseases (Le, Egondi, et al., 2014; Le, Pham, Do, & Do, 2014; Phung, Huang, Rutherford, Chu, Wang, Nguyen, Nguyen, & Do, 2015; Phung, Rutherford, et al., 2015). However, these studies did not examine AMI morbidity or mortality.
Previous studies have documented the relationship between ambient temperature and AMI HAs, and the findings are inconsistent (Bijelović et al., 2017; Fernández- García et al., 2015; Honda et al., 2016; Kwon et al., 2015; M. Loughnan et al., 2014;
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Mohammadi et al., 2018; Morabito et al., 2006; Ravljen et al., 2018; Tian et al., 2016; Wijnbergen et al., 2012; Yamaji et al., 2018). The current study supports the hypothesis of a positive association between high and low temperatures and AMI admissions in different climate zones. Regarding different conclusions in relation to the association between temperature and coronary heart diseases (including AMI) from various studies, this is likely to be a consequence of using differential temperature thresholds and different climate regions (Pudpong & Hajat, 2011). It appears that people are able to adapt to their local climate through physiological acclimatisation, behaviour patterns, and adaptive mechanisms (Medina-Ramón, Zanobetti, Cavanagh, & Schwartz, 2006).
There may also be some differences in terms of the statistical approaches used. The temperature threshold (where the risk of AMI admissions was lowest within the 5th to 95th temperature range) in the South Central Coast region (28.6oC) found in the current study is similar to the threshold (29.6oC) for increased CVDs morbidity found in Ho Chi Minh City (also in a tropical savanna climate zone) (Phung, Guo, Thai, et al., 2016); whereas the temperature threshold in North Central Coast region was 21.2oC. It should also be noted that the temperature threshold in a study in Thai Nguyen (Pham et al., 2014) – a province in the North of Vietnam in humid subtropical climate zone – was 29oC. These differences could partly explain some of the inconsistencies in results arising from the use of different statistical techniques to select the thresholds for building DLNM models (Pham et al., 2014). Other differences in statistical approaches include Pham, Do, Kim, Hac & Rocklöv’s (2014) study, where they chose to investigate the temperature-CVD admissions using specific estimates of heat and cold slopes with a ‘V’-shaped piecewise linear exposure-response relationship, whereas the current study and Phung, Guo, Thai, et al.’s (2016) study used spline functions to estimate non-linear exposure-response of such association.
A particular strength of the current study is that it has investigated the relationship between temperature and a specific CVDs (AMI), whilst the majority of previous studies have examined CVDs in general, including two recent studies in Vietnam (Pham et al., 2014; Phung, Guo, Thai, et al., 2016). In addition, AMI admissions were checked and verified by manually examining medical records providing a unique and high-quality health data set. Previous studies have relied on the
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accuracy of electronic medical records. Details of the arduous work undertaken to verify the medical records were provided in Chapter 3.
Another strength of this study is that influenza-like illness numbers were included and controlled for when assessing the association between temperature and AMI hospitalisation, which has not been addressed in previous studies in Vietnam (Pham et al., 2014; Phung, Guo, Thai, et al., 2016). Even though little evidence of the association between influenza and AMI at individual-level studies was found, there is consistent ecologic evidence that acute respiratory infection—influenza in particular—can trigger AMI (Finelli & Chaves, 2011; Warren-Gash et al., 2011; Warren-Gash et al., 2009). Inflammation plays an important role in the pathophysiology of premature atherosclerosis and CVDs, especially AMI. Vascular events may be related to short-term alterations of endothelial function and vascular relaxation, and these states could cause changes in the composition of atherosclerotic plaques (Long et al., 2018). Influenza with concomitant leucocytosis and cytokine response may precipitate atheroma instability and subsequent plaque rupture, causing vascular events in persons who have had fairly stable states of atherosclerosis (Finelli & Chaves, 2011). Thus, although more evidence is required, it is recommended that such analyses take into account the effect of influenza circulation when estimating the effect of temperature on AMI HAs.
This study has several limitations. First, the data were obtained from three hospitals, which may not be fully representative of all hospitals in the studied regions. These three hospitals are the highest-level and largest hospitals and take in most AMI patients from all around the research locations. However, the estimates in this study did not cover AMI patients who were not admitted or referred to these three hospitals. Secondly, the study utilised HAs data with limited information about individual exposure to temperature or information about adaptive measures (e.g., air conditioning), or any information on occupation or how much time the patients spent outdoors. These factors might reflect the actual exposure to high or low temperatures. Moreover, temperature and other meteorological data were collected from the main monitoring station in each province, which may not reflect the exact exposure of the local population. The selection bias might lead to underestimation of the associations, whereas information bias may make the observed associations less precise.
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6.6 CONCLUSION
In conclusion, the present study found an increase in risk of AMI admissions in relation to high temperatures in the South Central Coast region, while there was evidence of a cold effect in the North Central Coast region. Public health preparedness and multi-level interventions should attempt to reduce exposure to extremes of temperature. This may include readiness of cardiovascular intervention units for more visits, health education programs about AMI and its link to the ambient temperature for vulnerable groups, and highlighting the health risks in weather forecast where extremes conditions are imminent.
This chapter discussed the findings of an increase in the risk of AMI HAs for relatively high and low temperature ranges. The next chapter explores these results further and presents a detailed analysis of the added effects of heat waves and cold spells on admissions for AMI.
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Chapter 7: Added Heat Wave and Cold Spell Effects on Hospital Admissions of Acute Myocardial Infarction in Central Vietnam
Chapter 6 examined the short-term effects of temperature on acute myocardial infarction (AMI) hospital admissions (HAs) and showed that there was a significant increase in AMI HAs for high ambient temperatures in the South Central Coast region. Meanwhile, AMI HAs significantly increased for relatively low ambient temperatures in the North Central Coast region. Aiming to explore more in these links, this chapter quantifies the added effects of heat waves and cold spells on AMI admissions in these two areas. This is important, because the current study show that these extreme temperatures significantly increase the risk of AMI HAs.
7.1 INTRODUCTION
Health adverse effects from extreme temperature events have been studied worldwide. They have been documented as the most dangerous of all atmospheric hazards (at least in the mid-latitudes) and claim the largest numbers of sufferers, especially in relation to heat waves (Gabriel & Endlicher, 2011; Gosling, Lowe, McGregor, Pelling, & Malamud, 2009; Kyselý & Kříž, 2008; McMichael, Woodruff, & Hales, 2006; Sheridan, Kalkstein, & Kalkstein, 2009; Urban et al., 2014). People with cardiovascular or respiratory diseases, diabetes, chronic mental disorders, or other pre-existing medical conditions are at greater risk from heat exposure (Huang et al., 2011; Kovats & Hajat, 2008; World Health Organization, 2009). Moreover, Ye et al. (2012) claimed that the relationship between extreme temperature and excess morbidity was found to be much less consistent than in the case of mortality, and the range of affected diseases was more diverse.
In a review article on the effects of ambient temperature and morbidity, the authors found that the majority of studies reported a significant relationship between ambient temperature and total or cause-specific morbidities; however, there were some inconsistencies in the direction and magnitude of the effects (Ye et al., 2012). Looking at recent literature on this topic, a majority of studies found a significantly increased
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risk of cardiovascular disease (CVDs) mortality and morbidity due to ambient temperature extremes (Davídkovová, Plavcová, Kynčl, & Kyselý, 2014; Y. Guo et al., 2013; L. Webb, Bambrick, Tait, Green, & Alexander, 2014). For example, compared with the identified optimal temperature at 23.0°C, the cumulative relative risk for AMI HAs during 0 to 21 lag days was 2.38 (95% confidence interval, 1.83–3.10) for extreme cold (first percentile) and 1.49 (95% confidence interval, 1.24–1.80) for moderate cold temperature (10th percentile) (Tian et al., 2016). In another study, Davídkovová et al. (2014) found excess AMI mortality rates during cold spells and heat waves. However, Phung et al. (2017) conducted a meta-analysis of heat wave effect on CVDs admission across 25 cities of Vietnam and found no significant relationships.
Vietnam, a tropical developing country, is one of the countries in South-East Asia most vulnerable to climate change related hazards (Rubin, 2014; A.Yusuf & Francisco, 2009). Temperatures of over 40oC have been recorded in a majority of areas in Vietnam, and the frequency of days with an mean temperature above 35oC will be elevated in the future in highly vulnerable regions such as the Mekong Delta area (Phung et al., 2017). In addition, previous studies have indicated that extreme temperatures are linked with an increase in the risk of water- and vector-borne diseases, cardiovascular and respiratory diseases, and the risk of HAs among young children (Pham et al., 2014; Le, Egondi, et al., 2014; Le, Pham, et al., 2014; Phung, Guo, Nguyen, et al., 2016; Phung, Guo, Thai, et al., 2016; Phung, Huang, Rutherford, Chu, Wang, Nguyen, Nguyen, & Do, 2015; Phung, Rutherford, et al., 2015). However, these studies were conducted mostly in the southern or northern regions of Vietnam and focussed on CVDs and total mortality (Le, Egondi, et al., 2014; Phung, Guo, Thai, et al., 2016), they did not examine any evidence for a cold spell effect. A study by Phung, Guo, Thai, et al. (2015a) indicated that heat wave events caused a 12.9% increase in the risk of hospitalisation due to cardiovascular diseases (CVDs) in Ho Chi Minh City (Southern Vietnam), and another study in Hanoi found that heat waves were associated with a 36% increase in risk of HAs due to mental disorders in Hanoi City (Northern Vietnam) (Phung et al., 2017). However, such evidence is lacking for the Central region, and in particular, in regards to AMI (Phung et al., 2017).
This chapter investigates the impact of extreme temperature on HAs for AMI under different temperature metrics in two separate regions of the Central Coast area
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of Vietnam. A range of percentiles of daily mean temperature (1st, 2nd, 3rd, 4th, 5th, 95th, 96th, 97th, 98th, and 99th) and different number of consecutive days (i.e., ≥ 2, ≥ 3, and ≥ 4 consecutive days) were used to define a cold spell or heat wave. Heat waves and cold spells were defined separately by regions, as the North and South Central Coast regions of Vietnam have different climate zones with distinct climate patterns.
In order to evaluate the effect of heat waves or cold spells on the risk of AMI hospitalisation, the analysis employed generalised linear models (GLMs) and distributed lag non-linear models (DLNM) (Gasparrini et al., 2010a). A negative binomial distribution was used to account for over-dispersion. A 24-hour mean temperature was chosen for the analysis. The effect of heat waves or cold spells on the IRR of AMI HAs was estimated adjusting for long-term trends, seasonality, weekend and holiday, influenza-like illness incidence, and other meteorological factors. Two- stage modelling was conducted to separately examine the influence of temperature after adjusting for the effect of other influences. The first stage examined the influence of ambient temperature and other influences, while the second stage fitted the residuals from the previous model to estimate the effect of temperature extremes on AMI HAs.
Subgroup analyses were also performed to evaluate whether there was any effect modification related to patient characteristics (i.e., gender, age, and disease status). In a sensitivity analysis, we changed different degrees of freedom for the splines, cross- basis of DLNM and of other meteorological variables; changed the maximum lag; changed to use the data set without 2013 records and changed to use one-stage model. Details of the analysis for estimating the added effect of temperature extremes on AMI HAs were provided in Sections 3.6.5 and 3.6.6 of Chapter 3.
7.2 HEAT WAVE EFFECT IN THE SOUTH CENTRAL COAST OF VIETNAM
Figure 7.1 shows the relative risk of AMI patients who experienced heat waves. Among the 15 definitions of heat waves used (Table 3.5 in Chapter 3), results from the 97th temperature percentile for three consecutive days showed a steady increase in the relative risk of AMI admissions. In particular, the relative risk at the 98th temperature percentile for three consecutive days appeared to significantly increase (1.22, 95% CI: 1.04–1.44), p = 0.017). In addition, the relative risk of admissions ranged from 0.93 (95% CI: 0.84–1.03, p = 0.164) to 1.30 (95% CI: 0.98–1.72, p = 0.064), which showed that the risk appeared to be sensitive to the heat wave definition and may have affected
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the measurement of the relationship between heat waves and this health outcome (Figure 7.1).
Figure 7.1 The relative risk at exposed day of AMI patients by different heat wave definitions before hospitalisation during the period 2008–2015 in the South Central Coast region, Vietnam
Note: Estimates presented are the relative risk of AMI admissions for the heat wave event (compared to no event) at lag 0 after adjusting for ambient temperature, humidity, air pressure, wind speed, and other factors.
The heat wave definition was selected using the 98th temperature percentile and at least three consecutive days for further analysis about the heat wave added effect. This is because, as shown in Figure 7.1, this definition was the only one that showed a significant association with AMI HAs in the South Central Coast region. The results based on other heat wave definitions are summarised in Appendix N.
For the heat wave definition using the 98th temperature percentile and at least three consecutive days, the heat wave effect was found to be most acute for the first three days of exposure, the largest relative risk being at lag1 (1.32, 95% CI: 1.05–1.66, p = 0.017) in Figure 7.2. This figure also indicates that the extreme heat effect continuously decreased before lag 7.
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Figure 7.2 Estimates of the relative risk for the heat wave effect at the 98th temperature percentile with three consecutive days on AMI admissions at different lags
Note: the blue dotted line represents relative risk equal to 1.00.
A heat wave effect was also investigated among different subgroups using the heat wave defined previously (as three or more consecutive days of temperature above the 98th percentile). The overall risk of AMI HAs was 22.0% (RR, 1.22; 95%CI, 1.04– 1.44, p = 0.017) higher during the heat wave event (at the day of exposure) than those not exposed to heatwaves. Most of the subgroups showed an increase in the risk of AMI admissions when exposed to a heat wave event, with the exception of female patients (p = 0.574). Among those results, male AMI patients (p = 0.001), AMI patients younger than 65 (p = 0.041), patients with STEMI (p = 0.004), or with no comorbidity (p = 0.019) presented a statistically significant increase in relative risk of AMI admissions. In terms of gender, the risk of AMI HAs for males significantly increased 53% (RR, 1.53; 95%CI, 1.18-1.99, p = 0.001), while the change in females was not significant (RR, 0.85; 95%CI, 0.49-1.47, p = 0.574). In terms of age groups, the risk of AMI admissions was significantly increased by 48% (RR, 1.48; 95%CI, 1.02-2.18, p = 0.041) among people aged 18-64, whereas the results were not significant for the older group (RR, 1.22; 95%CI, 0.91-1.64, p = 0.180). Patients with STEMI had a significant increase of 59% (RR, 1.59; 95% CI, 1.16-2.19, p = 0.004), while the group with Non-STEMI showed no significant result (RR, 1.09; 95%CI, 0.77-1.54, p = 0.633). For comorbidity status, patients without comorbidity experienced a significantly higher risk of AMI HAs during the heat waves by 56% (RR, 1.56, 95%CI,
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1.07-2.26, p = 0.020), while for patients with comorbidity this increase was 19% (RR: 1.19, 95%CI, 0.87-1.62, p = 0.270) but not significant.
The effect of heat waves on AMI HAs varied slightly by subgroups when the definition of a heat wave was changed; however, male and STEMI groups remained significantly associated with an increase of risk of AMI HAs in most of the definitions (see Appendix Q). The sensitivity analyses results showed various significant levels of temperature-AMI relationships using different definitions (see Appendix O). The results of the sensitivity analysis for the acute and prolonged effects also changed remarkably when: 1) controlling for trend and seasonality by using ns(time, 6*year); 2) changing maximum lag periods (i.e., maxlag of either 14, 21, and 30); 3) setting up cross-basis with 6 degree of freedom; or 4) controlling for meteorological factors with 6 degree of freedom. Otherwise, the results were similar when other approaches of sensitivity analysis were used (see Appendix P)
Figure 7.3 Relative risk of daily AMI admissions associated with the added heat wave effect at the temperature of the 98th percentile with three consecutive days for different subgroups
7.3 COLD SPELL EFFECT IN THE NORTH CENTRAL COAST OF VIETNAM
There were 1,986 AMI HAs from 2008 to 2015 (2,922 days) at Hue Central Hospital and Vietnam-Cuba Friendships Hospital in the North Central Coast region. The number of daily AMI admissions ranged from 0 to 5, with a mean of 0.7.
Figure 7.4 shows the relative risk of AMI patients who experienced cold spells. The 15 definitions for cold spell outlined in Table 3.6 (in chapter 3) were used, and all results from the threshold of 5th temperature percentile and lower observed an increase in the relative risk of AMI admissions after controlling for other variables. There was a clear growing trend of cold spell effects with lower thresholds and higher number of
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consecutive days. Particularly, at threshold of 2nd and 1st temperature percentiles, the risk of AMI admissions appeared to significantly increase after a cold spell event. The cumulated relative risk for the first 14 days after exposure increased 35% (95% CI: 1.03–1.76, p = 0.028) and 68% (95% CI: 1.10–2.56, p = 0.016) with cold spell at 2nd and 1st for three consecutive days, respectively. In addition, the relative risk of admissions ranged from 1.10 (95% CI: 0.93–1.27, p = 0.233) to 1.79 (95% CI: 1.12– 2.86, p = 0.015), which showed that the change in cold spell definitions could affect the assessment of the relationship between cold spell and AMI admissions (Figure 7.4).
Figure 7.4 The cumulative relative risks for the first 14 days of AMI patients by different cold spell definitions before hospitalisation during the period 2008–2015 in the North Central Coast region, Vietnam
Note: Estimates presented are after adjusting for ambient temperature, humidity, air pressure, wind speed, and other factors.
In terms of estimating the cold spell effect along lags and among different subgroups, the definition of three or more consecutive days with a temperature below the 2nd temperature percentile was used. As in Figure 7.4, this definition was the first one with three consecutive days that showed a significant association with AMI HAs, and the significance remained sustainable in following definitions. The results based on other cold spell definitions are summarised in Appendix R.
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The low extreme temperature effect was found to be delayed when the risk of AMI admissions increased from lag 4 days after exposure; however, the results were significantly high after two weeks of exposure, the largest relative risk was at lag 18 (1.41, 95% CI: 1.09-1.82, p = 0.009) (Figure 7.5).
Figure 7.5 Estimates of the relative risk for the cold spell effect at the 2nd temperature percentile with three consecutive days on AMI admissions at different lags
All subgroups showed an increase in the risk of AMI admissions after exposure to this cold spell event. However, only male and older groups show statistically significant increases. Regarding gender, male AMI patients showed a significantly increased risk of AMI HAs, by 51% (RR, 1.51; 95%CI, 1.10-2.08, p = 0.011) during the cold spells; while the risk for the female group found no significant increase in HAs (RR, 1.04; 95%CI, 0.64-1.71, p = 0.875). In terms of age groups, the risk of AMI admissions was significantly higher among people aged 65+, with a 45% increase (RR, 1.45; 95%CI, 1.08-1.95, p = 0.014); whereas this figure for younger age groups was 6% (RR, 1.06; 95%CI, 0.60-1.88, p = 0.855), but not significant. Other subgroups showed an increased risk of AMI HAs during and after 14 days of cold spells exposure; however, the results were not found to be significant for the STEMI group (p = 0.692), Non-STEMI group (p = 0.226), the group with previous CVDs (p = 0.195), those without previous CVDs (p = 0.922), the group with at least one comorbidity (p = 0.494), and patients without a comorbidity (p = 0.380).
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The effects of a cold spell on AMI varied slightly by subgroups when the definition of a cold spell event was changed; however, males and the ≥ 65 years old group remained significantly associated with an increase of risk of AMI HAs in most of the cold spell definitions (see Appendix V). The sensitivity analyses results showed various significant levels of temperature-AMI relationship in different definitions (see Appendix S). The results of sensitivity analysis for the delayed and prolonged effects also changed remarkably when: 1) controlling for trend and seasonality by using ns(time, 6*year), or 2) changing maximum lag period. Otherwise, the results were similar to other approaches used in the sensitivity analysis (see Appendix T).
Figure 7.6 Relative risk of daily AMI admissions associated with the added cold spell effect at the temperature of the 2nd percentile with three consecutive days for different subgroups
7.4 DISCUSSION
Significant heat wave and cold spell effects were found for an increasing risk of AMI HAs in the South Central Coast and the North Central Coast areas respectively. A substantial increase in risk was found for males, the younger age group, and those with STEMI in relation to heat wave exposure; and in males and the elderly group in regards to cold spell exposure. The results indicate that the effect of extreme temperatures on AMI hospitalisation varied between these two regions of the Central Vietnam. However, it is worth noting that the results changed with the different definitions of heat wave or cold spells used, which consequently reduces the ability to generalise these findings to heat waves or cold spells in general (as those might be defined differently).
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In the South-Central Coast region, a significant added effect of heat waves temperature ≥ 30.4oC (98th percentile of mean temperature) sustained for ≥ 3 days, was found among selected groups. This finding is consistent with the findings from previous investigations (Madrigano et al., 2013). The current study indicated that HAs due to AMI increased by 44% (6%–74%) during the two days after a heat wave, with the largest effect of heat wave on AMI admissions on the same-day exposure. Currently, a limited number of studies have focussed on heat waves and CVDs admissions. One study was conducted in Ho Chi Minh City, in the south of Vietnam, but only focused on CVDs admissions during the heat waves period and showed a significant increase of the risk due to heat waves (Phung, Guo, Thai, et al., 2016). However, in a later study estimating the pooled effect for 25 cities across Vietnam, the authors concluded that the risk of hospitalisation for CVDs due to heat waves was insignificant in Vietnam (Phung et al., 2017). These contradictory results could be due to the fact that the results of the studies were from pooled effect sizes, or because the definition of heat waves they used was based on a heat index, which is an apparent temperature taking into account the humidity in the indicator. In addition, amongst the 25 cities, there were no representatives from the North Central Coast region (as included in the current study).
It is also worth mentioning the potential interactions between increased air pollution with extreme heat. This might be particularly important in the more urban region of south central Vietnam. Air pollution and heat both have been linked to AMI onset. (Claeys et al., 2017; Nuvolone et al., 2011; Bhaskaran et al., 2009; Bhaskaran et al., 2009). However, the interaction between heat and air pollution on AMI incidence has not been clearly established. There appears to be no unidirectional influence of air pollution in heat-health relationships. Moreover, there are conflicting findings in academic literature from different fields regarding this relationship. Some studies found that air pollution is not a confounder; other studies discovered that air pollution is a confounder and air pollutants have a possible synergistic effect with heat on mortality, or even interacts with temperature (Rainham and Smoyer-Tomic, 2003; O'Neill et al., 2005; Filleul et al., 2006; Stafoggia, 2006; Basu et al., 2008; Bell et al., 2008; Vaneckova et al., 2008; Zanobetti and Schwartz, 2008; Lam, 2014). Moreover, the combination of two triggers, low air temperature and air pollution, might have an additive effect, as was
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observed during the Great Smog of London. This may be at least partly, because the increase use of domestic heating (fossil fuels) on cold days increases air pollution, as assumed by Claeys et al. (2017). Therefore, further investigation and communication are necessary to ensure a more holistic understanding of the air pollution-heat/cold- health relationship.
The results from the current study show that a significant short lag effect (0-2 days) of heat waves aligned with the lag effect reported from previous findings (0- 3days) (Huang, Barnett, Wang, & Tong, 2012; Lin et al., 2009; Nitschke, Tucker, & Bi, 2007; Phung, Guo, Thai, et al., 2016; Schwartz, Samet, & Patz, 2004). Moreover, the current research shows a similar result to that of Phung and Guo (2016) showing a higher risk for patients aged less than 65. However, the results differed from that of previous studies that indicated that the elderly were more vulnerable to high temperatures (Koken et al., 2003; Linares & Díaz, 2008; Michelozzi et al., 2009; Schwartz et al., 2004). This result may be explained by the assumption that, in Vietnam, younger people are more likely to perform labouring work outdoors and suffer from heat wave exposure more than elderly people, who do not go out and benefit from better conditions at home during extreme temperature events. This explanation is supported by the evidence that males or people without a comorbidity had a higher risk of AMI than females or people with a comorbidity during heat waves (see Figure 7.3). The modification effects of occupation on the temperature and AMI relationship, especially in different tropical regions, should be an area of further investigation. The current study also found that patients with STEMI were likely to be at higher risk for HAs during a heat wave.
In contrast to the heat wave effect in the South Central Coast region, the North Central Coast region showed only a cold spell effect for AMI hospitalisation, and several differences were found within subgroups. The results revealed signs of elevated risk for AMI admissions for all of the cold spell definitions with the threshold temperature of 5th and below. For the purpose of quantifying the effect, the cold spell that was defined as temperature ≤ 15,5oC (2nd percentile of mean temperature) sustained for ≥ 3 days was chosen for the analyses. In contrast to the short duration of the heat effect and its concentration on days of elevated ambient temperatures, the impacts of cold spells on AMI admissions were less pronounced and persisted for a longer period after the end of a cold spell. The results indicate an elevated risk for AMI
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admissions after three days of cold spell exposure (RR 1%, 95%CI: 0.83–1.21, p = 0.962) and this was statistically significant higher from a lag of 13 days (RR 31%, 95%CI: 1.01–1.71).
The absence of excess HAs (on days 0-2 during cold spells) is a somewhat counter intuitive finding and may have several causes. The first possible explanation is that it may be due to a longer delay in hospitalisations (Bhaskaran et al., 2010). The delay in seeking medical care and HAs can be up to a week from the first onset of symptoms (Gravely‐Witte, Jurgens, Tamim, & Grace, 2010). Additionally, the hospitals examined in the current study were the highest-level hospitals in the studied regions; thus, AMI patients hospitalised at lower level hospitals were likely to take time to be referred to these hospitals. Further analysis into delayed time in relation to a cold spell period showed that there were signs of a longer delayed effect of AMI HAs in the group of patients initially hospitalised at lower level hospitals compared to the effect in groups initially hospitalised at provincial or central level hospitals. The increase of AMI HAs started at a lag of 10 days in the former group (lower level hospitals) compared to lag 0 in the latter group (provincial or central level hospitals). However, both results were not significant, which might be due to the limited number of AMI patients hospitalised during cold spells in each group (11 vs 22 cases in the former and latter groups, respectively) (see Appendix W).
While a lagged cold effect for mortality of up to two weeks was found in a number of studies (Carder et al., 2005; Hajat, Kovats, & Lachowycz, 2007; Kyselý, Plavcová, Davídkovová, & Kynčl, 2011), the results of studies that examined a lagged cold effect on HAs are inconsistent (Bayentin et al., 2010; Bhaskaran et al., 2010; Morabito et al., 2006; Morabito et al., 2005; Schwartz et al., 2004; Urban et al., 2014).
In the current study, with respect to gender and age and the cumulative risk of AMI HAs for two weeks after exposure to cold spells, the risk significantly increased for the older age group (65+ years) by 45% (RR 1.45, 95% CI: 1.08–1.95), while the younger age group showed no significant increase for the risk (RR 1.06, 95% CI: 0.60– 1.88, p = 0.855). This finding is consistent with results from previous studies for aggregated CVDs mortality that showed that low temperature extremes affect cardiovascular health more markedly in the older population compared to the younger age groups (Gómez-Acebo, Llorca, & Dierssen, 2013; Vaičiulis, Radišauskas, & Vasilavičius, 2016). In addition, the results of the current study showed another
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significantly vulnerable group for cold spells in regard to increased risk of AMI HAs, as males were found to be at a higher risk of 51% (RR 1.51, 95% CI: 1.10–2.08) during cold spell exposure than those not exposed to cold spells. No significant increase for the risk of AMI HAs was found due to cold spells in other sub-groups. The risk calculated for STEMI, Non-STEMI groups, those with a history of CVDs and those with none, patients with a least one comorbidity and those without a comorbidity were 1.09 (95% CI: 0.73–1.62, p = 0.692), 1.34 (95% CI: 0.83–2.17, p = 0.226), 1.30 (95% CI: 0.88–1.91, p = 0.195), 1.03 (95% CI: 0.62–1.69, p = 0.922), 1.16 (95% CI: 0.77– 1.76, p = 0.494), and 1.23 (95% CI: 0.78–1.95, p = 0.380), respectively. However, as mentioned previously, the results for this subgroup analysis changed based on the threshold used; thus, caution is required when interpreting and making conclusions and recommendations based on these results.
A strength of this study is the careful data collection process, especially the confirmation of the AMI diagnosis, which helped to reduce the noise of using online hospital databases, which are common in Vietnamese hospital record systems. Moreover, the DLNM model was used, which allowed for the characterisation of the effect of exposure on the health outcome on both non-linear and delay aspects; thus, enabling assessment of the association of temperature extremes and AMI HAs in terms of exposure to extreme events, as well as the delayed effect of these extreme events.
This study has several limitations. First, it is plausible that data may be missing from the AMI admissions, especially among persons who died out of hospital or did not reach the studied hospitals. This is a limitation for any studies using hospital records. The outdoor monitoring data were used to represent the population exposure to ambient temperature, which may induce exposure misclassifications, this is also the common limitation of time-series environment-health research. Finally, and importantly, caution is warranted when the findings of this study are generalised to other locations with different climates and population characteristics. The misclassification bias might lead to an underestimation of the associations found, and the information bias would make the observed associations less precise.
Despite these limitations, this study provides important evidence in regard to the relationship between extreme weather events and AMI admissions in the Central Coast of Vietnam. Evidence-based temperature change adaptation and intervention programs are required to enhance the capacity of the health sector, such as community education
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to reduce the influence of extreme temperatures. This evidence and information is required to develop early warning systems for temperature-caused health risks, establishing shelters for vulnerable groups and residents in temperature disadvantaged locations to reduce the burden of diseases and to protect residents from the effects of extreme temperature events. In addition, a study projecting the social and economic burden of AMI associated with extreme weather events using different climate change scenarios should also be conducted to provide important data for public health policy makers and practitioners to enhance preparedness and practical prevention in the future.
7.5 CONCLUSION
This chapter explored the added effect of extreme events on AMI HAs in separate climate zones in Central Vietnam in detail. Some evidence was found for significant heat wave and cold spell effects in relation to an increasing risk of AMI HAs in the South Central Coast (tropical savanna climate) and North Central Coast areas (tropical monsoon climate), respectively. Moreover, there was some evidence that males, younger age groups, those with STEMI, and patients without a comorbidity showed significant increases in the risk of AMI admissions in relation to heat waves in the South Central Coast region; while only males and the elderly group were more prone to AMI admissions in relation to cold spells in the North Central Coast region.
The following chapter provides a general discussion of the results and the conclusions for the thesis.
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Chapter 8: General Discussion and Recommendations
This final chapter summarises the research findings regarding ambient temperature impacts on hospital admissions (HAs) among adult acute myocardial infarction (AMI) patients. The chapter begins by describing how the findings contribute to the literature about temperature and AMI hospitalisation. The limitations of the research are then discussed and suggestions for future research that would be useful in Vietnam and similar settings are then offered. Recommendations for addressing temperature impacts on AMI hospitalisation and reducing delays from AMI incidents to HAs in Vietnam are also described.
8.1 KEY FINDINGS
8.1.1 Long-term trends and seasonality of hospital admissions due to acute myocardial infarction A number of international studies have examined the associations of seasonality on AMI morbidity (Bijelovic et al., 2017; Honda et al., 2016; Mohammadi et al., 2018; Morabito et al., 2006; Yamaji et al., 2016), of which a majority showed a significant increase in risk at low temperatures (Bijelovic et al., 2017; Honda et al., 2016; Misailidou et al., 2006; Ravljen et al., 2018; Tian et al., 2015). However, trends in AMI incidence and hospitalisations vary, with some studies showing peaks in high temperature periods (Kynast-Wolf, Preuß, Sié, Kouyat, & Becher, 2010; Loughnan et al., 2010b). Differences between studies could be related to many factors, including different methodological approaches (Xun et al., 2010), geographic differences on the impact of climate, the ability of the human body to acclimate, and its adaptive behaviour patterns (Bijelovic et al., 2017).
For the Central Coast of Vietnam, this study found a general increase in the number of AMI HAs across the three provinces, with an increase of 10% in Khanh Hoa, 5% in Thua Thien Hue, and 12% in Quang Binh per year from 2008 to 2015. Increases of this magnitude are probably not related to demographic changes in those provinces, as the population in central Vietnam is relatively stable over time, with low levels of inward and outward migration. The most likely explanation is that these
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provinces underwent significant socio-economic development during this time, which parallels a developing trend nationwide. All three provinces had increases in monthly income and reductions in the poverty rate (see details in Appendix G). The provincial governments of all three provinces were able to invest more money in advanced health care facilities and improved systems for patient transfer to and between primary health care services and hospitals.
The increase in patient numbers might also be related to general health and lifestyle changes, as the population has been suffering from increased non- communicable diseases (T. Nguyen & Hoang, 2018; Rocklöv et al., 2014). In general, Vietnamese people have reduced their daily activity levels, increased consumption of many foods and additives, including salt, low-quality instant foods, and sweetened non-alcoholic beverages. Alcohol consumption and smoking prevalence are very high in men (T. Nguyen & Hoang, 2018). In national surveys (STEPS 2010 and 2015), a comparison of results showed that, apart from decreases in vegetable and fruit intake and less time for physical activities, there have been increases in alcohol consumption, the proportion of overweight people, and those with elevated cholesterol levels (Department of preventive medicine-Vietnamese Ministry of Health, 2016). Details of these results are shown in Appendix F. The rate of male smokers barely changed from 47.7% in 2010 to 45.3% in 2015, as indicated by the Global Adult Tobacco surveys (World Health Organization, 2015a). Across these national surveys (STEPS and GATS), there are several risk factors that have much higher rates in men compared to women, such as alcohol consumption (77.3% vs 11.0%) (Department of preventive medicine-Vietnamese Ministry of Health, 2016), tobacco smoking (45.3% vs 1.1%) (GATS 2015) and having less vegetables or fruits per day as recommended by WHO 2013 (World Health Organization, 2013) (63.2% vs 51.5%) (Department of preventive medicine-Vietnamese Ministry of Health, 2016). The data have not been disaggregated for the population of central Vietnamese provinces, but there is little reason to expect the patterns would differ from the national picture.
In terms of the seasonality of AMI HAs, overall, the results showed that AMI HAs were significantly higher in winter compared to summer. The risk of AMI HAs in winter was estimated to increase by 24%, 37%, and 43% in Khanh Hoa, Thua Thien Hue, and Quang Binh, respectively. The period from January to February appeared to
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be a consistent peak of AMI hospitalisations, while the period from July to August appeared to consistently show the lowest number of hospitalisations.
When examining the seasonality of AMI HAs among different age groups and gender, the results indicated differences between the two climate zones. The effect of winter on AMI HAs gradually reduced from the north to the south within each subgroup. There was a significantly higher incidence of AMI HAs among people aged 65+ during winter in all three studied provinces compared to summer, the number of winter events increasing by 54% (24%–93%), 41% (21%–65%), and 25% (7%–46%) in Quang Binh, Thua Thien Hue, and Khanh Hoa, respectively. In terms of gender, in the South Central Coast region, the risk of AMI HAs for males was significantly 28% (7%–52%) higher during winter than during summer. In contrast, females had a significantly increased risk of AMI HAs during winter in Thua Thien Hue (were 64% (32%–101%) higher) and Quang Binh (68% (20%–137%) higher), respectively).
8.1.2 Short-term effects of ambient temperature on hospital admissions due to acute myocardial infarction Contributing to the literature about associated factors for AMI occurrence, the results of the current study found evidence that ambient temperature was associated with AMI onset in different ways. Both heat and cold effects were found on AMI HAs; however, these differed between regions in the Central Coast region of Vietnam, according to climate zones. More precisely, there were significantly more HAs when the ambient temperature was high in the South Central Coast region (savanna tropical climate), while a significantly higher rate of AMI hospitalisation for lower temperatures was found in the North Central Coast region (monsoon tropical climate).
In the North Central Coast region, the risk of AMI HAs was higher 11% (RR: 1.11, 95% CI: 0.91–1.35, p > 0.05) at a moderate low temperature (18.5oC) than the reference temperatures (21.2oC). Moreover, the risk was 25% (cumulative RR: 1.25, 95% CI: 1.02–1.55, p < 0.05) higher at extreme low temperatures (16.8oC) than the reference temperature. Whereas, in South Central Coast region, the risk of AMI HAs was 18% (RR: 1.18, 95% CI: 0.95–1.47, p > 0.05) and 36% (RR: 1.36, 95% CI: 1.06– 1.73, p < 0.05) higher at moderately high temperatures (29.5oC) and at extreme high temperature (29.9oC) than the reference temperature (23.4oC).
This finding contributes to understanding of associations between temperature and HAs due to AMI in Vietnam, especially for the central regions. Other relevant
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studies in Vietnam have only focused on broad health categories such as CHA (Pham et al., 2014, Phung et al., 2016), CVDs, water- and vector-borne diseases or mortality in general (Le, Egondi, et al., 2014; Le, Pham, Do, & Do, 2014; Phung, Huang, Rutherford, Chu, Wang, Nguyen, Nguyen, & Do, 2015; Phung, Rutherford, et al., 2015). In relation to the global evidence base, this study supports the hypothesis of a positive association between high and low temperatures and AMI admissions in different climate zones. It may be add some clarity to inconsistent findings on the relationship between ambient temperature and AMI HAs (Bijelović et al., 2017; Fernández-García et al., 2015; Honda et al., 2016; Kwon et al., 2015; M. Loughnan et al., 2014; Mohammadi et al., 2018; Morabito et al., 2006; Ravljen et al., 2018; Tian et al., 2016; Wijnbergen et al., 2012; Yamaji et al., 2018). An important task for future research is to synthesise the evidence to examine the extent to which association between temperature and coronary heart diseases (including AMI) may be explained by differential temperature thresholds and different climate regions (Pudpong & Hajat, 2011).
With regard to the delayed effects of high and low temperatures on AMI HAs, it is likely that very high temperatures had an immediate impact, whereas low temperatures induced a more delayed impact. The effect of cold appeared to last more than three weeks in the North Central Coast region, while the heat effect appeared to last a shorter period of time in the South-Central Coast region. In addition, in relation to the subgroups analysed, it is likely there were those with the elderly, Non-STEMI groups, or having at least one comorbidity vulnerable to heat, while the detrimental impact of cold temperature was significantly higher in male patients and the elderly.
8.1.3 Added effects of temperature extremes on the hospital admissions due to acute myocardial infarction The most recently developed definitions for heat waves and cold spells were adopted in this study compared to those using absolute temperature or thresholds. An absolute temperature may be fine to use; however, it is not reasonable for comparing results from different climate zones. Moreover, using different definitions allowed the analysis to be more robust and to also explore at what temperature and duration the association was evident.
In this research, the heat wave or cold spell matrix included a single intensity (e.g., from 95th to 99th percentile of daily mean temperature for heat waves and from
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1st to 5th of daily mean temperature for cold spells) and a fixed duration (e.g., ≥ 2, ≥ 3, and ≥ 4 consecutive days). A significant effect was found for the definition for heat waves consisting of the 98th percentile of temperature for three consecutive days, while for cold spells it was the 2nd percentile of temperature for three consecutive days. Other heat wave definitions explored did not appear to show significant evidence for an association. Regarding cold spell definitions, for thresholds at the 5th, 4th, and 3rd, only a duration of four consecutive days made the association significant; while other definitions following the 2nd temperature percentile for three consecutive days or more were all established as a significant association. Hence, the definition at the 2nd temperature percentile for three consecutive days or more was used for further analysis regarding the added effect of cold spells on AMI HAs.
Using the above definitions, on days of heat wave exposure in the South-Central Coast region, the rate of HAs for AMI was 22% (95% CI: 4%–44%) higher than those not have heat waves. This effect was found to be acute when the risk of AMI admissions was significantly higher than for the first three days of exposure. On the other hand, on the days of cold spell exposure in the North Central Coast region, the rate of HAs for AMI was 35% (95% CI: 3%–76%) higher. The cold spell’s adverse effects were delayed compared to the acute detrimental effect of heat waves. The risk of AMI admissions increased after four days of exposure to the cold spell event and remained high after three weeks.
Groups found to be vulnerable to heat waves in this research were males (risk increased by 34% [11%–62%]) and patients with STEMI (59% [16%–119%]). On the other hand, males (51% [10%–108%]) and the elderly were found to be significantly vulnerable to cold spells, with a 45% (8%–95%) increase in the risk of AMI for people aged 65+ compared to younger individuals. Males were recognised as having higher risk for AMI onset when exposed to either a heat wave or cold spell. This was assumed to be due to the fact that men suffered more from risk factors of CVDs (significantly higher rates for smoking, alcohol consumption, having less vegetables/fruits– (Department of preventive medicine-Vietnamese Ministry of Health, 2016). Moreover, sociocultural norms related to gender may have an effect. In Vietnam, men are still strongly expected to provide the main income for the family. This work will often be conducted outdoors and will likely be performed regardless of adverse weather conditions. Consequently, men are likely to be more exposed to those
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temperature extremes, potentially raising the risk of AMI onset. Similarly, younger age groups and people without disabilities or significant existing comorbidities are more likely to be physical labourers and therefore may be directly exposed to more extremes of temperature.
Regarding the high risk in the advanced age groups during cold spell exposure, trends in central Vietnam are similar to a number of studies showing that low temperature extremes affect cardiovascular health more markedly in the older population compared to the younger age groups (Chau, Wong, & Woo, 2014; L. Webb et al., 2014). In terms of sex difference, our finding that males were more vulnerable to both heatwaves and cold spells is consistent with studies of Loughnan et al. (2010) and Mohammad et al. (2018) where males were susceptible to the heat. In contrast, Radisauska et al. (2014) and Kwon et al. (2015) found females are more vulnerable to cold weather or disadvantageous temperature, respectively. Others found no sex differences in the associations of temperature and AMI morbidity (Misailidou et al., 2006; Abrignani et al., 2009; Wichmann et al., 2013. Thus, the findings of the current study regarding population sub-groups being most vulnerable to heat waves and cold spells may be valuable in terms of health education programs for sensitising people to the risk of AMI due to climate extremes, and where possible, to support workplace health and safety guidelines to reduce occupational exposure.
8.1.4 Pre-hospital delay time of patients with acute myocardial infarction Nearly half (46.1%) of the AMI patients in this study were found to present to medical centres 12 hours or more after the onset of the event. This estimate is slightly higher than a recent estimation of 42% from a study conducted at the National Heart Institute in Vietnam (H. Nguyen et al., 2018). This alarming rate of delay poses major risks for AMI patients because they do not receive sufficient reperfusion treatments— thrombolytic therapy and percutaneous coronary intervention—which are optimal and clinically recommended within the first 12 hours from onset of symptoms. The situation is serious for many patients because the delay time data here relates to the lag from onset time to the time of presentation at the first medical centre; this does not include delays due to transfer from low-level medical centres to high-level hospitals (provincial and central hospitals) to receive reperfusion treatments. Thus, the percentage of AMI patients arriving at high-level hospitals within 12 hours from the onset for reperfusion is likely to be lower than 54%.
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Efficacy of reperfusion therapies depends directly on the time from AMI onset to the treatment provision (Fibrinolytic Therapy Trialists, 1994; Nallamothu et al., 2007). The survival rate is very high when treatment is initiated within an hour of the onset and it dramatically reduces after six hours. The survival rate is about 50% if the thrombolytic medications is provided within an hour and is decreased to 28% after three hours (Fibrinolytic Therapy Trialists, 1994; Nallamothu et al., 2007). The delay time to treatment evident in these data strongly underscores the need for community health education programs to sensitise people to the early signs of heart attack, and the need for improved patient transport to enable expedited admissions to tertiary care centres.
The current research also found a number of independent predictors for pre- hospital delay. The following groups were found to be likely to have relatively long pre-hospital delay time: women, older age groups, AMI sufferers hospitalised at low- level medical centres, residents of Quang Binh (compared to Khanh Hoa), patients ranked with a low score (score I compared to IV) for Killip classification, and patients with at least one comorbidity (see Chapter 4). Patients ranked as I for the Killip classification, assuming that they had a less severe health condition, were more likely to delay their hospitalisation in comparison to those ranked as IV (an intense life- threatening condition). It was hypothesised that perceiving severe life-threatening signs/symptoms made carers hurry to hospital. However, recorded Killip classification in the current study was conducted at the admitted hospitals, it is therefore possible that the longer delay to hospitals could have exacerbated the severity of the health condition (higher Killip classification score). Therefore, using the Killip classification as an explanatory factor for pre-hospital delay time for AMI patients is problematic.
In this study, there were more AMI patients in Quang Binh province who delayed hospitalisation compared to Khanh Hoa. This could have arisen because Quang Binh is a province more economically disadvantaged than Khanh Hoa; it has lower average SES, larger land area, and a more rural population. Details of comparison of provincial characteristics between the two provinces are shown in Appendix G. Longer delay times may have occurred in this province because patients may not have had transportation to get to a medical centre and they may have needed to travel further to get to the medical centre. However, the existing data were not
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sufficient to determine the precise reasons for differences between provinces in delay time.
AMI patients initially visiting low-level medical centres (such as commune health centres) were found to be more likely to experience a delay in hospital treatment compared to those who went directly to hospital and were hospitalised at central/provincial level hospitals. It was assumed that those hospitalised at high level facilities were likely to live near those hospitals in urban areas and they may have had a higher SES. They may also have had access to ambulances that are not usually available to people in local commune and district level centres in rural and remote areas.
8.2 STRENGTHS AND LIMITATIONS
8.2.1 Strengths This study has three key strengths. First, to the best of my knowledge, this is the first multicity study to quantify temperature variation and extremes of AMI morbidity in Vietnam. There have been a limited number of studies undertaken in Vietnam about temperature and health effects, and these have mostly investigated broader health outcomes, such as all-cause mortality or cardiovascular disease (CVDs) in general (Dang et al., 2016; Pham et al., 2014; Phung et al., 2017; Phung, Guo, Nguyen, et al., 2016; Phung, Guo, Thai, et al., 2016). Second, a high-quality health data set was obtained through a careful manual process for AMI case selection. Data were directly collected from hard-copy medical records of AMI stored in the hospitals; thus, the quality of the data was better in terms of completeness and accuracy than other studies that have only used electronic databases, where the accuracy of information (in the under-resourced context of Vietnamese hospitals) can be uncertain and difficult to verify. The present data collection process is important in Vietnam at this time, because health record storage in hospitals (both physically and electronically) has many limitations. Third, the study used different analytical strategies to achieve the study’s objectives, programmes such as EpiData v3.1 to manage the data, and SPSS v23 and R v3.2.5 software to analyse the data. Multivariable logistic regression models were used to identify correlates of pre-hospital delay time, generalised linear regression, and distributed lag non-linear models were used to estimate the short-term effect of temperature variation and added effects of temperature extremes on AMI
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HAs. The application of these advanced statistical methods helped to provide sophisticated answers to the research questions. EpiData 3.1 was also used to limit errors in data entry and management. A further strength is that this is the first such study in Vietnam to control for influenza when exploring the temperature effect on human health. Respiratory infections might lead to an onset of AMI through the suppression of immune responses (Finelli & Chaves, 2011; Warren-Gash et al., 2011; Warren-Gash et al., 2009). Indeed, during the analysis, influenza counts were significantly associated with AMI HAs; therefore, influenza appeared to be an important variable when assessing the relationship between ambient temperature and AMI HAs in this study at the population level. Last but not least, it is worth mentioning that AMI delay analysis has potential to contribute to health system strengthening efforts through health service improvement.
8.2.2 Limitations The research has several limitations. The first limitation is the study design. Although the time-series design using secondary data is the most commonly used and appropriate method for estimating temperature effects on health, the limitation of this design is that it does not enable clear determination of a causal relationship between measured variables and AMI HAs. Moreover, the study used HAs data, which has limited information in terms of individual characteristics, such as demographics, disease conditions, and individual direct temperature exposure. For example, when exploring the pre-hospital delay time determinants, potentially important information about patients’ socio-economic status, the signs and the severity of the disease, absence of companion/medical attendant/escort, or distance from home to hospitals was not able to be obtained. Likewise, information on exposure to temperature or information on adaptive measures (e.g., air conditioning), or any information about occupation or how much time people spent outdoors, which might reflect the actual exposure to high or low temperature, was also not available. In addition, collecting information on modifiable risk factors, such as physical exercise, smoking, or alcohol consumption is important for interpreting the associations between ambient temperature on AMI HAs in general. Unfortunately, these data were not able to be captured in this study. Therefore, the study could not include analysis of the aetiological role of temperature in relation to other traditional risk factors for AMI.
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Another related limitation is that approximately 10% of the medical records coded as chronic ischemic heart disease (I25–ICD 10) were eligible to be AMI cases. However, the number of I25-ICD10 records was huge (approximately 20,000 medical records for the eight-year period for these hospitals) and it was not possible (in terms of the time and cost) to audit them. Therefore, the I25-coded medical records were not assessed to collect another 10% of the HAs eligible for AMI (ICD 10–I21) in this group. In addition, 11.7% of records were missing among hard copy medical records with AMI discharge diagnosis across the three hospitals. The missing data may have reduced the ability to detect the effect of ambient temperature or extremes on AMI HAs.
A further important limitation of this study is that the data were collected from only three hospitals. They were the biggest and highest-level hospitals in the selected provinces and were reported by various local experts to take in about 80% of surviving AMI patients from the provinces. Thus, if one in five such cases was missed, the conclusions from the analysis may not be representative of all AMI patients in these regions. The results may also be limited to patients who made it to hospital alive. Up to 60% of AMI sufferers either die or fully recover naturally within a couple of hours after the occurrence (Taghaddosi et al., 2010). Some AMI patients who stay at home or stay in a lower level hospital due to various reasons (e.g. lack of diagnosis, severe health condition, poverty, advanced age, and infirmity, etc.) were not included. This may have reduced the ability to detect the effect of temperature variations or extremes on AMI HAs, because the number of cases in various subgroups (such as people from remote areas) may have been limited.
In terms of meteorological data collection, temperature and other meteorological data measurements were obtained from the most central monitoring station in each province. Thus, the analyses were conducted under the assumption that all of the residents of the province were exposed to the same ambient meteorological factors. However, this approach may not reflect the exact exposure of affected individuals due to variations in geographical and suburban characteristics.
Finally, another limitation of this research is that the modifying effect of air pollutants on the association of temperature and AMI HAs were not able to be controlled, as there is a lack of appropriate monitoring for air pollutants in central Vietnam. In a related data set obtained from the National Centre for Environmental
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Monitoring (Centre for Environmental Monitoring (Vietnam Environment Administration), 2016), there were several air pollutant datasets for two of the three studied provinces for only three years 2013-2015. Moreover, these datasets had an average of 18.4% (4.1%–35.5%) missing data. This seriously limited the ability to analyse air pollution, and as such, it was excluded from the study. It is acknowledged that variations in air quality could be important in contributing to AMI morbidity, as has been found in other studies (Peters, Dockery, Muller, & Mittleman, 2001; Shrey et al., 2011; Yamaji et al., 2017).
8.2.3 Sources of bias and confounding Acknowledging the limitation above, two of the main sources of bias that have been emphasized in this study are the presence of unmeasured confounders, and error due to the use of data on temperature from fixed sites rather than individual exposure. Such bias is inevitable, particularly for the latter because temperature exposure of specific individuals who have AMI is usually not recorded. The fixed-point data serves as a proxy measure. Fortunately, time-series analysis helps to minimise the such bias in ecological designs, especially when investigating acute effects of environmental stressors (ambient temperature) that change over time. Unmeasured individual factors such as diet or smoking tend to vary only slowly over time, so their effects are filtered out by the smooth function of time in the DLNM models. Moreover, variation in exposure across individuals is not the problem. While there may be large variation in temperature over an area, changes in daily population averages are usually well indicated by the central monitor. This implies that the error in assigning individuals to central site levels is primarily of a Berkson-type, which in linear models does not lead to bias in estimates of effect in linear models, though it does reduce the precision (Gasparrini and Armstrong, 2010b).
Another concern is that AMI misclassification is likely. Some error of this time is inevitable in context of middle income countries where high quality of medical records cannot be assured. However, such misclassification should not be associated with the day-to-day variation in ambient temperature that form the exposure side of the equation in time series studies.
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Possible confounding due to air pollution should also be mentioned. Air pollution and temperature both have some certain impacts on AMI onset. (Nuvolone et al., 2011; Bhaskaran et al., 2009a; Bhaskaran et al., 2009b). However, the interaction of temperature and air pollution on AMI incidence has not been clearly established. Only a few studies investigated whether ozone and PMx may interact with air temperature to affect mortality (Basu et al., 2008; Park et al., 2011; Pattenden et al., 2010; Qian et al., 2008; Stafoggia et al., 2008b; Zanobetti and Schwartz, 2008). Pollutants may amplify the effects of high temperatures on mortality (Ren et al., 2006, 2008). Breitner et al. (2014) found that adjusting for air pollution (ozone) is linked with larger percent increases in the effect of ambient temperature on CVD mortality. Wichmann et al. (2013) explored that after adjusting for PM10, NOx the association between ambient temperature and AMI HAs strengthened. Therefore, we assumed that the effect of temperature on AMI HAs found in our study might be have increased after adjusting for air pollutants, especially with PMx and NOx.
8.3 CONTRIBUTIONS OF THIS THESIS
This is the first study to explore the effects of ambient temperatures on AMI occurrence in Vietnam. Although several related studies have been undertaken, their focus was on mortality or CVDs in general (Pham et al., 2014; Phung et al., 2016; Phung et al., 2017; Xuan et al., 2014). Previous evidence has shown that while heat stress increases mortality, especially due to severe exacerbation of chronic CVDs (atherosclerosis, chronic ischemic heart diseases), the effects of cold stress are most pronounced on acute CVDs (MI). The different responses of individual CVDs to heat and cold stress represents an important finding that needs to be confirmed for other populations, and it may be useful for issuing better-targeted heat- and cold-stress alerts (Urban et al., 2012). Thus, this study contributes new insight into temperature impacts on AMI morbidity in a Vietnamese context.
Among the studies that have focussed on temperature and AMI globally, no predominant pattern has emerged regarding adverse effects of temperature on AMI hospitalisations. There have been inconsistent results (i.e., cold effect, heat effect, both heat and cold effects, and no effect) observed across regions, countries, latitudes, and climate zones (Bijelović et al., 2017; Fernández-García et al.; Honda et al., 2016; Kwon et al., 2015; Ravljen et al., 2018; Tian et al., 2016; Loughnan et al., 2014; Mohammadi et al., 2018; Morabito et al., 2006; Wijnbergen et al., 2012; Yamaji et al.,
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2017). The current study demonstrated both cold and heat effects on AMI onset in separate regions, according to different climate zones in a primarily tropical-climate developing country. While many studies have shown that the risk of AMI is related to cold exposure (Madrigano et al., 2013; Tian et al., 2016), some research has found the increased risk of AMI related to extreme heat exposure (Kwon et al., 2015; Urban et al., 2014). There are quite clear and biologically plausible mechanisms linking cold exposure to atherothrombotic events, including AMI (Claeys et al., 2017); whereas mechanisms linking heat exposure to AMI onset remain unclear. The findings of the current study regarding the increased risk of AMI due to heat exposure contributes to the literature supporting the adverse heat effect on AMI occurrence. This evidence provides motivation for further research exploring the biological mechanisms of the impact or population characteristics that make them vulnerable to this impact.
Variation in ambient temperatures should be recognised as an important trigger for an AMI episode. It is an important proximal factor that should be considered alongside the well-established distal risk factors (e.g., smoking, lack of physical exertion, family history, etc.). Even though temperature is not generally recognised as an important risk factor for the development of AMI at the individual level, the public health relevance is considerable, as the environment impacts a large number of people on a continuous and involuntary basis. Studies that have calculated a population attributable fraction of various triggers of AMI have found that temperature has a comparable population attributable fraction (5%-10% in temperature climate regions to cold regions) to other well-accepted triggers for myocardial infarction, such as physical exertion and alcohol (Claeys et al., 2017; Nawrot, Perez, Künzli, Munters, & Nemery, 2011). Therefore, the role of temperature as an important trigger of AMI should be documented in medical textbooks, enhancing the awareness of this trigger toward AMI among general practitioners and cardiologists.
A further contribution is that this study is among limited research in Vietnam and the Mekong Delta countries regarding delay in hospital arrival after AMI onset. It contributes by demonstrating that a significantly high proportion of AMI patients had delayed presentation to high quality medical care. Therefore, the findings emphasise the urgent need for measures to reduce the duration of the pre-hospital delay of AMI patients, especially as the disorder is becoming more common in Vietnam.
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8.4 IMPLICATIONS FOR FURTHER RESEARCH
As electronic databases of hospital records in Vietnam are not well-established in hospitals, researchers who would like to collect electronic health data should be aware of concerns about its completeness and accuracy.
Due to the limited number of AMI HAs among some subgroups, existing associations between temperature extremes and AMI in some specific subgroups might not have been detected in this study. Therefore, similar studies in the future should cover more sites or gather records over a longer period to collect sufficient data for analysis. Moreover, to estimate the effects of environmental factors on this health outcome, spatial-temporal analyses are recommended to learn more about how the effects vary by time and location. Moreover, in addition to calculating relative risk, research should estimate the burden of the disease due to temperature extremes.
Combined environmental indicators may be useful, such as a heat index, physiologically equivalent temperature, and apparent temperature. These indicators take into account meteorological factors other than temperature itself, such as humidity and air pressure. These indicators recognise that the onset of a disease is generally the result of a complex process involving multiple factors.
To reduce measurement bias related to temperature exposure, researchers could employ or use meteorological data of closer monitoring stations/ satellite’s data / algorithm-based extrapolation to estimate the meteorological data of AMI patients’ neighbourhood. Moreover, a cohort study where each participant is provided a device that obtains the surrounding meteorological data should be conducted to collect details in terms of individual exposure (P. Webb, 2017).
For research into the pre-hospital delay time for AMI hospitalisation, researchers should focus on the interval from the onset of first symptoms to the presentation time at high-level hospitals where patients can receive reperfusion treatment. In the Vietnamese context, patients can receive this treatment only at provincial and central level hospitals or the equivalent, and not in local commune health centres.
A limited number of studies have explored the awareness of local populations regarding adverse impacts of temperature extremes or variations and related adaptive activities and behaviours. There is a need to evaluate how knowledge of temperature impacts on AMI occurrence can be effectively translated into preventative measures
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or improve the efficiency and distribution of resources during anticipated increases in AMI occurrence (e.g., increases in the availability of health care resources during high risk periods). Future research should do more to address these aspects.
Last but not least, reflection on the aetiological role of climate factors in relation to other traditional risk factors for AMI was outside the scope of this Thesis. However, future research should explore more clearly the role of temperature as a co-variate for traditional behavioural and social risk factors for AMI.
8.5 RECOMMENDATIONS
For the general public, individuals should adequately prepare themselves against disadvantageous temperature changes (i.e., wear reasonable clothing, follow weather forecasts in the media), and avoid temperature stress (i.e., set up domestic heating or air conditioning, restrict outdoor physical exercise), particularly for people who have, or at high risk of having, CVDs. They should be educated about the potential danger from extreme of temperatures as a trigger for heart attacks, especially its early symptoms and the importance of early hospitalisation. For the local community, there is a need to put more effort into reducing exposure to disadvantageous temperatures (i.e., building green spaces in the residential areas).
For universities, research institutes, and heart associations; more research is required on other regions in Vietnam in different climate zones to explore the associated factors for AMI pre-hospital delay time. More evidence is required about the association between temperatures (as well as other weather elements or air pollution) in regards to AMI to inform adaptation strategies to cope with the adverse impacts. In the future, some forecasting or modelling could be done to assess how temperature changes (with and without other weather factors) could interact to influence the geographic range, alter seasonal patterns, and affect the intensity of AMI burdens. Additionally, academic bodies and heart associations could provide relevant guidelines, recommendations, and document the temperature as an important environmental trigger for AMI.
For hospitals, there is a need to establish electronic databases, pay attention to maintaining high quality databases, and create and maintain efficient links between databases nationwide. This will help to reduce the current huge manual effort of collecting medical records and related documentation. Health service managers need
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to emphasise the importance of data collection, with careful training of Health Information Management staff who undertake the role of online data entry. This is essential to produce effective and accurate electronic databases. Further, this study indicates that it could be beneficial for the health sector to routinely link hospital data with meteorological data to monitor trends in the burden of disease related to climate variation.
Government health authorities should develop legislation and national strategies, to enable better forecasting of health crises related to disadvantageous temperature. Regarding protection and control of NCDs nationwide, from the 2000s, the Vietnamese Government and Ministry of Health have issued and implemented a wide range of strategies, plans, policies and national programmes (Ministry of Health Vietnam, 2015; Vietnam Goverment, 2015). However, the targeted activities primarily have focused on controlling traditional risk factors for NCDs (i.e., tobacco smoking, alcohol consumption, unreasonable food intake, physical inactivity, overweight/obesity, glucose blood increase) (Ministry of Health Vietnam, 2015). The updated national strategy on prevention and control of NCDs (including CVDs) period 2015-2025, issued by the Vietnamese Prime Minster, and the plan for prevention and control of NCDs (including CVDs) period 2015-2020, issued by the Minister of Health, although amended, still did not mention adverse effects of temperature on CVDs (Ministry of Health Vietnam, 2015; Vietnam Goverment, 2015). With an increase in the quality of evidence regarding disadvantageous temperatures triggering CVDs, a section containing recommendations for how to reduce the negative effect of temperature on CVDs needs to be added to these strategies.
Weather forecasts, especially real-time reports relating to cold spells and heat waves, could easily be made available in local media (newspaper, television, Internet) and could be based upon validated small area monitoring systems. Weather forecasting is reasonably accurate up to a week in advance, and this information is widely available in Vietnam. However, there is a lack of health recommendations corresponding to the weather forecasting and health, including in relation to CVDs and AMI. Therefore, given emerging evidence regarding the relationship between temperature and AMI, susceptible groups (i.e., males who work outdoors, the elderly, people living with comorbid health conditions) could be warned when the AMI risk is likely to elevate, and they can be given advice to reduce their personal risk (via daily weather
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forecasting programs). In rural Vietnam, authorities or employers could use the existing public loudspeaker system to inform farmers about temperature extremes to avoid extreme exposures when they are working in fields.
Aside from the effects of ambient temperature and AMI onset, the importance of a shortened hospital arrival time for AMI sufferers’ survival and better recovery is worth noting. This study found that a very high proportion of patients experienced long delays before receiving effective treatments. In order to improve the treatment outcomes for AMI, there is a need for an education program about AMI risk factors (both traditional and environmental factors), signs, and symptoms and the importance of presenting to AMI “eligible-treatment” hospitals as soon as possible.
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Appendices
APPENDIX A IMPACT OF AMBIENT TEMPERATURE ON EMERGENCY HOSPITAL ADMISSIONS FOR ACUTE MYOCARDIAL INFARCTION IN CENTRAL VIETNAM (MEDICAL DATA COLLECTION SHEET)
Notice: Fill in the blanks or circle ONE number referred to your best answer.
A. GENERAL INFORMATION 1. Gender: 1.Male 2. Female 2. Year of birth: 19…………. (Age at hospitalization:………..) 3. Living location: …………………………………………………………………………………...... 4. Occupation: 1. Unemployed 2. Others:………….. 5. Having Health Insurance: 1. Yes 2. No B. HOSPITAL ADMISSION FOR ACUTE MYOCARDIAL INFARCTION The first health contact (HF) 6. Level of the HF: 1.commune 2.district 3.city 4.provincial 5. central 6. Private clinics 7. Date of HF admission (dd/mm/yy): …………………………………………. 8. Period of time from the first signs/symptoms occurred to arrival at the first HF: 1. Under 6h 2. 6–12h 3. 13–24h 4. More than 24h (…….…..days) 9. Date having first signs/symptoms (dd/mm/yyyy):……………………… Hospitalization at this provincial/central hospital 10. Date of admissions (dd/mm/yyyy): …………………………….. 11. Would referring to Hue Central Hospital? 1. No 2. Yes 12. Had the patient hospitalized within 28 days due to AIM before this hospitalization? 1. Yes 2.No If the answer of Q11 OR Q12 is “Yes”, DO NOT continue to answer further questions in the sheet.
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13. Reasons for this 1 Not for PCI 2 For PCI Hospitalisation 14. AMI status: 1 STEMI 2 Non-STEMI 15. Infarction sites 1 anterior 2 lateral 3 septal 4 inferior (Multiple answers 5 Right ventricle 9 N/A are allowed) 9 16. Killip class 1 Class I 02 Class II 3 Class III 04 Class IV 17. Pre-existing CVDs: 1 Yes 2 No 18. Co-morbidity (Multiple answers are allowed) 1 Hypertension 2 Diabetes 3 Dyslipidemia 4 Coronary heart 5 Other:…………………… 9 N/A disease 9 19. Treatments 1 Medications 2 PCI 3 CAGB 20. Health condition at 1 Good 2 Bad 3 Death discharge:
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APPENDIX B IMPACT OF AMBIENT TEMPERATURE ON EMERGENCY HOSPITAL ADMISSIONS FOR ACUTE MYOCARDIAL INFARCTION IN CENTRAL VIETNAM (MEDICAL DATA COLLECTION SHEET) (Vietnamese Version)
Chú ý: Điền thông tin hoặc khoanh tròn vào 01 kết quả cho mỗi câu hỏi trong phiếu điều tra. A. THÔNG TIN CHUNG 1. Giới tính: 1.Nam 2.Nữ 2. Năm sinh: 19…….. (Tuổi lúc vào viện:……………….) 3. Nơi sinh sống (xã/phường): ………………………………………………………… 4. Nghề nghiệp: 1. MSLĐ 2. Khác:……………………………. 5. Bảohiểm y tế : 1. Có 2.Không B. NHẬP VIỆN DO NHỒI MÁU CƠ TIM Nhập viện tại cơ sở y tế cấp cứu đầu tiên 6. Tuyến bệnh viện: 1.Xã/phường 2.Huyện 3.Thành phố 4.Tỉnh 5.Trung ương 6. Phòng khám tư 7. Ngày nhập viện (ngày/tháng/năm): …………………………………………. 8. Thời gian từ khi xuất hiện dấu hiệu bệnh đầu tiên đến khi vào cơ sở y tế cấp cứu đầu tiên: 1. Dưới 6 giờ 2. 6–12 giờ 3.13–24 giờ 4. Trên 24 giờ (………ngày) 9. Ngày xuất hiện dấu hiệu bệnh đầu tiên (ngày/tháng/năm): .…………..……………… Nhập viện tại bệnh viện tuyến Tỉnh/Trung Ương (Nơi lấy số liệu) 10. Ngày nhập viện (ngày/tháng/năm): …………………………………………. 11. Chuyển tuyến lên bệnh viện TƯ Huế 1. Không 2. Có 12. Nhập viện trước đây do NMCTC trong vòng 28 ngày tính từ ngày đầu tiên nhập viện của lần NMCTC này? 1.Không 2. Có Nếu câu 11 hoặc 12 có đáp án là “CÓ”, KHÔNG điền tiếp các phần còn lại.
Appendices 187
13. Phân loại NMCTC 1 không do thủ thuật MV 2 Do thủ thuật MV 14. Phân loại NMCTC: 1 ST chênh lên 2 ST không chênh lên 15. Vùng NMCT: (Có thể 1 Trước 2 Bên 3 Vách 4 Dưới chọn nhiều đáp án) 5 Thất phải 99 Không xác định 16. Phân độ Killip 1 Độ I 2 Độ II 3 Độ III 4 Độ IV 17. Tiền sử bệnh tim mạch: 1 Có: 2 Không ………………………… 18. Bệnh lý kèm theo:(Có thể chọn nhiều đáp án) 1 Tăng huyết áp 2 Đái tháo đường 3 Rối loạn Lipid máu 4 Bệnh mạch vành 5 Khác:…………………… 99 Không xác định …… 19. Điều trị 1 Nội Khoa 2 Can thiệp MV 3 Bắt cầu nối VC 20. Tình trạng sức khoẻ lúc 1 Tốt 2 Xấu 3 Tử vong ra viện:
188 Appendices
APPENDIX C ETHICS APPROVAL OF UNIVERSITY HUMAN ETHICS COMMITTEE OF HUE UNIVERSITY OF MEDICINE AND PHARMACY
Appendices 189
(English translated)
190 Appendices
APPENDIX D ETHICS APPROVAL OF UNIVERSITY HUMAN ETHICS COMMITTEE OF QUEENSLAND UNIVERSITY OF TECHNOLOGY
Appendices 191
APPENDIX E SEVERAL ACTIVITIES OF MEDICAL DATA COLLECTION
Note: a- principle researcher visited medical record storage in a hospital; b–research assistants at Vietnam-Cuba Friendships Hospital; c–searching and collecting hard copy medical records in hospital storage; d–qualified doctor helped to select eligible AMI medical records; e–research assistants helped to transfer required information from the hard copy records to medical data collection sheets.
192 Appendices
APPENDIX F COMPARATION OF MAIN RESULTS OF STEPS 2010 AND STEPS 2015 FOR 25-64 YEARS OLD POPULATION, VIETNAM
Characteristics STEPS 2015 STEPS 2010 % % 95% CI 95% CI Total Male Female Total Male Female Had alcohol in 44.8 80.3 11.2 37.0 69.6 5.6 the past 30 days 42.4–47.3 77.7–82.9 9.2–13.2 36.5–37.5 68.6–70.6 5.2–6.0 Had < 5 serves of 57.2 63.2 51.5 81.7 81.6 81.7 vegetable +/-fruit 54.6–59.6 59.8–66.7 48.1–54.9 81.1–82.3 78.8–82.4 80.9–82.5 per day Had < 150mins 26.1 19.0 32.6 30.4 28.1 32.6 for moderate 23.9–28.3 16.5–21.6 29.7–35.6 29.8–31.0 27.2–29.0 31.8–33.4 physical activities in a week or equivalence Overweight 17.5 16.9 18.1 12.0 12.5 11.4 (BMI ≥ 25 15.5–19.5 14.0–19.7 15.7–20.6 11.2–12.8 11.2–13.8 10.4–12.4 kg/m2) Had a total blood 32.4 27.9 36.7 30.1 27.8 32.3 cholesterol 29.8–35.0 24.4–31.4 33.4–40.0 29.5–30.7 26.8–28.8 31.5–33.1 increased (≥ 5.0 mmol/l or ≥ 190 mg/dl) or taking medications to reduce cholesterol levels Note: STEPS–STEPwise approach for NCD risk factor surveillance; BMI–Body Mass Index (Source: National survey on risk factors of non-communicable disease in Vietnam, 2015) (Department of preventive medicine-Vietnamese Ministry of Health, 2016))
Appendices 193
APPENDIX G COMPARATION OF MAIN SOCIO-ECONOMIC FACTORS IN STUDIED PROVINCES BETWEEN 2008 AND 2015
Quang Binh Thua Thien Hue Khanh Hoa Characteristics 2008 2015 2008 2015 2008 2015 Area (Km2)* 8065.3 8065.3 5033.2 5033.2 5217.7 5217.7 Population (x 1000)* 853.4 872.9 1103.1 1140.7 1171.4 1205.3 Population density (person/km2)* 105.8 108.0 219.2 227.0 224.5 231.0 Male 421.5 436.9 533.8 566.3 570.4 594.2 % Male 50.0 50.1 49.2 49.6 49.6 49.3 Female 422.0 436.0 551.1 575.4 578.9 611.1 % Female 50.0 49.9 50.8 50.4 50.4 50.7 Urban pop 125.3 170.9 383.5 555.2 452.5 541.3 Urbanization rate 14.9 19.6 35.3 48.6 39.4 44.9 Rural pop 718.2 702.0 701.4 586.5 696.8 664.0 Rural rate 85.1 80.4 64.7 51.4 60.6 55.1 Population increase rate 0.6 0.6 0.4 0.9 1.0 0.7 Illiterate ≥ 15 year-olds (%)* 3 2.9 7.9 7.4 5.8 5.2 Area for rice (x 1000ha) 50.8 54.1 53.1 54.4 45.7 34.2 Area of grain crops (x 1000ha) 55.4 58.9 25.5 56.0 51.4 40.3 Water surface for aquaculture (x 1000ha) 3.9 5.1 5.5 7.2 6.1 5.2 Health establishments 173 174.0 181 179.0 164 166 Hospitals 8 8 13 17 10 12 Health staff 1590 2274 2028 2154 2603 3155 General Practitioners (GPs) and specialised doctors 362 556.0 606 620.0 742 633 2-year trained GPs 445 592.0 510 440.0 566 755 Nurses 447 772.0 486 711.0 896 1251 Midwives 336 354.0 426 383.0 399 516 Monthly average income per capital (x 1000VND)a* 1180 2043 1402.5 2384 1577 2787 Poverty rate (%) 21.9 12.5 13.7 4.7 9.1 5 Immigration rate (%) 1.5 3.2 3.1 3.0 3.5 1.6 Migration rate (%) 6.9 6.6 8.8 8.4 3.9 4.9 Net migration rate (%) -5.4 -3.4 -5.8 -5.4 -0.4 -3.3 Note: * data of 2011, a data were average of the previous and the following years; VND: Vietnam dong (Vietnamese currency)
194 Appendices
APPENDIX H CORRELATIONS BETWEEN DAILY AMI HOSPITAL ADMISSIONS AND AMBIENT TEMPERATURE AND OTHER ENVIROMENTAL FACTORS
North Central Coast
Appendices 195
South Central Coast
196 Appendices
APPENDIX K OVERALL EFFECTS OF TEMPRATURE ON AMI HOSPITAL ADMISSIONS IN THE SOUTH CENTRAL COAST REGION IN SENSITIVITY ANALYSIS
Appendices 197
198 Appendices
Appendices 199
Summary of Case crossover model: daily AMI HAs ~ strata(time) + mean temperature + relative humidity + air pressure + wind speed n= 5893, number of events= 1342 coef exp(coef) se(coef) z Pr(>|z|) temperature -0.0152781 0.9848380 0.0375881 -0.406 0.684 humidity 0.0058396 1.0058566 0.0077058 0.758 0.449 wind speed -0.0006478 0.9993524 0.0284849 -0.023 0.982 air pressure -0.0140209 0.9860769 0.0185006 -0.758 0.449 exp(coef) exp(-coef) lower .95 upper .95 temperature 0.9848 1.0154 0.9149 1.060 humidity 1.0059 0.9942 0.9908 1.021 wind speed 0.9994 1.0006 0.9451 1.057 air pressure 0.9861 1.0141 0.9510 1.022 Rsquare= 0 (max possible= 0.516 ) Likelihood ratio test= 1.92 on 4 df, p=0.7499 Wald test = 1.93 on 4 df, p=0.7493 Score (logrank) test = 1.93 on 4 df, p=0.7491
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APPENDIX L OVERALL EFFECTS OF TEMPRATURE ON AMI HOSPITAL ADMISSIONS IN THE NORTH CENTRAL COAST REGION IN SENSITIVITY ANALYSIS
Appendices 201
202 Appendices
Appendices 203
Summary of Case crossover model: daily AMI HAs ~ strata(time) + mean temperature + relative humidity + air pressure + wind speed n= 8708, number of events= 1986 coef exp(coef) se(coef) z Pr(>|z|) temperature 0.013384 1.013474 0.019016 0.704 0.482 humidity 0.001745 1.001747 0.005844 0.299 0.765 wind speed -0.026602 0.973749 0.043159 -0.616 0.538 air pressure 0.007612 1.007641 0.011902 0.640 0.522 exp(coef) exp(-coef) lower .95 upper .95 temperature 1.0135 0.9867 0.9764 1.052 humidity 1.0017 0.9983 0.9903 1.013 wind speed 0.9737 1.0270 0.8948 1.060 air pressure 1.0076 0.9924 0.9844 1.031 Rsquare= 0 (max possible= 0.527 ) Likelihood ratio test= 1.12 on 4 df, p=0.8904 Wald test = 1.12 on 4 df, p=0.8913 Score (logrank) test = 1.12 on 4 df, p=0.8912
204 Appendices
APPENDIX M NUMBER OF AMI HOSPITAL ADMISSIONS AMONG VARIOUS SUBGROUPS ON DAYS WITH A HEAT WAVE OR COLD SPELL AT DIFFERENT DEFINITIONS
Subgroup Total Male Female < 65 years ≥ 65 years Having No history STEMI Non- Having Have no HWDs old old CVDs in with STEMI comorbidity comorbidity history CVDs 1 53 32 21 18 35 33 20 27 26 36 17 2 36 26 10 14 22 21 15 21 15 21 15 3 35 26 9 14 21 20 15 20 15 20 15 4 48 31 17 17 31 29 19 24 24 33 15 5 32 25 7 13 19 19 13 19 13 18 14 6 31 25 6 13 18 18 13 18 13 17 14 7 40 26 14 12 28 24 16 20 20 26 14 8 28 23 5 10 18 16 12 15 13 15 13 9 27 22 5 10 17 15 12 14 13 15 12 10 26 22 4 9 17 15 11 13 13 14 12 11 24 20 4 9 15 13 11 12 12 13 11 12 13 11 2 4 9 9 4 3 10 8 5 13 15 14 1 6 9 11 4 4 11 8 7 14 12 11 1 6 6 8 4 3 9 7 5 15 9 8 1 3 6 6 3 1 8 6 3
Note: HWDs–Heat wave definitions; CVDs–Cardiovascular diseases; STEMI–ST segment elevation myocardial infarction.
Appendices 205
Subgroup Total Male Female < 65 ≥ 65 Having No history STEMI* Non- Having Have no CSDs CVDs in with STEMI* comorbidity comorbidity history* CVDs* * * 1 102 64 38 25 77 61 33 56 38 57 37 2 90 58 32 20 70 52 30 47 35 50 32 3 67 44 23 18 49 35 24 36 23 35 24 4 77 47 30 19 58 46 23 42 27 44 25 5 67 42 25 15 52 38 21 35 24 37 22 6 54 34 20 12 42 29 17 28 18 30 16 7 69 43 26 16 53 39 22 36 25 38 23 8 59 37 22 13 46 33 18 28 23 31 20 9 49 30 19 10 39 25 16 24 17 26 15 10 44 28 16 12 32 25 13 23 15 23 15 11 39 25 14 10 29 20 13 20 13 19 14 12 29 21 8 6 23 14 9 13 10 12 11 13 19 13 6 6 13 10 7 11 6 8 9 14 17 12 5 5 12 8 7 9 6 6 9 15 15 11 4 4 11 7 6 7 6 6 7
Notes: *–analysed on 1914 AMI cases in North Central Coast region (missing 72 cases did not cover the information of initial hospitalization); CSDs–Cold spell definitions; CVDs–Cardiovascular diseases; STEMI–ST segment elevation myocardial infarction.
206 Appendices
APPENDIX N RELATIVE RISKS FOR THE HEAT WAVE EFFECTS OF VARIOUS DEFINITIONS
Note: horizontal axe represents number of lag days (0-7 lag days), vertical axe represents relative risk (0.5–2.0), horizontal black lines were where relative risk is 1.0, grey areas were 95% CI. Definitions of cold spell based on percentiles of daily mean temperature (as thresholds) and number of consecutive days having temperature equal to or higher than those thresholds.
Appendices 207
APPENDIX O RELATIVE RISKS AT EXPOSED DAY OF AMI HOSPITAL ADMISSIONS BY DIFFERENT HEAT WAVE DEFINITIONS IN SENSITIVITY ANALSYS
208 Appendices
Appendices 209
210 Appendices
Appendices 211
APPENDIX P RELATIVE RISKS FOR ADDED HEAT WAVE EFFECTS AT 98th TEMPERATURE PERCENTILE WITH THREE CONSECUTIVE DAYS ON AMI HOSPITAL ADMISSIONS AT DIFFERENT LAGS IN SENSITIVITY ANALYSIS
212 Appendices
Appendices 213
214 Appendices
APPENDIX Q RELATIVE RISKS OF AMI HOSPITAL ADMISSIONS ASSOCIATED WITH THE ADDED EFFECT FOR DIFFERENT SUBGROUPS WITH VARIOUS DEFINITIONS OF HEAT WAVES
Appendices 215
216 Appendices
Appendices 217
218 Appendices
Appendices 219
220 Appendices
Appendices 221
APPENDIX R RELATIVE RISKS FOR THE COLD SPELL EFFECTS OF VARIOUS DEFINITIONS
Note: horizontal axe represents number of lag days (0-21 lag days), vertical axe represents relative risk (0.8–3.0), horizontal black lines were where relative risk is 1.0, grey areas were 95% CI. Definitions of cold spell based on percentiles of daily mean temperature (as thresholds) and number of consecutive days having temperature equal to or lower than those thresholds.
222 Appendices
APPENDIX S RELATIVE RISKS AT EXPOSED DAY OF AMI HOSPITAL ADMISSIONS BY DIFFERENT COLD SPELL DEFINITIONS IN SENSITIVITY ANALYSIS
Appendices 223
224 Appendices
Appendices 225
226 Appendices
APPENDIX T RELATIVE RISKS FOR ADDED COLD SPELL EFFECTS AT 2nd TEMPERATURE PERCENTILE WITH THREE CONSECUTIVE DAYS ON AMI HOSPITAL ADMISSIONS AT DIFFERENT LAGS IN SENSITIVITY ANALYSIS
Appendices 227
228 Appendices
Appendices 229
APPENDIX V RELATIVE RISKS OF AMI HOSPITAL ADMISSIONS ASSOCIATED WITH THE ADDED EFFECT FOR DIFFERENT SUBGROUPS WITH VARIOUS DEFINITIONS OF COLD SPELL
230 Appendices
Appendices 231
232 Appendices
Appendices 233
234 Appendices
Appendices 235
236 Appendices
APPENDIX W RELATIVE RISKS OF AMI HOSPITAL ADMISSIONS ASSOCIATED WITH THE ADDED COLD SPELL EFFECT AT 2nd TEMPERATURE PERCENTILE WITH THREE CONSECUTIVE DAYS OR MORE FOR PATIENTS INITIALLY HOSPITALISED AT PROVINCIAL OR CENTRAL LEVEL HOSPITALS AND THOSE WHO WERE NOT
Subgroup Initially hospitalised at Initially hospitalised at lower CSDs provincial/central level level hospitals* hospitals* 1 63 31 2 54 28 3 37 22 4 47 22 5 41 18 6 30 16 7 42 19 8 36 15 9 28 13 10 26 12 11 22 11 12 18 5 13 13 4 14 12 3 15 10 3
Notes: *– analyzed on 1914 AMI cases in North Central Coast region (missing 72 cases did not cover the information of initial hospitalization); CSDs–Cold spell definitions.
Appendices 237
238 Appendices