Table 1. Characteristic of included studies.

NO	Authors and years of publication	Events No /Data source	Location and time period	Main temperature exposure variable (s)	Variables Controlled	Lags (Days)	Study design	Effect estimate of temperature/threshold (definition of hot & cold effect)	Outcome measurement
1	Lin et al. (2013b)	1.253.75 mortality per day/Department of Health	Four regions of Taiwan 1994–2007	Mean temperature	PMa10, NObx, Oc3	0–20	Time-Series	15°C compared with 27°C for cold effect vs. 31°C compared with 27°C for hot effect	ICDd-9 Codes Ischemic heart disease and CVD
2	Tian et al. (2012)	26,460/Death Classification System, Beijing Public Security Bureau	Beijing, China 2000–2011	Daily mean temperature	Day of the week	0–15	Time-Series	99th (30.5°C) compared to 90th (27.0°C) for hot effect vs 1st (−7.6°C) compared to 10th (−2.2°C) for cold effect	ICD-10: (I20–I25). CHD mortality
3	Yu et al. (2011b)	22,805/Office of Economic and Statistical Research of the Queensland Treasury	Queensland, Australia 1996–2004	Mean temperature	Time trend, PM10, RHe,NO2, O3	0–20	Time-Series	1°C above 24°C for hot effect 1°C below 24°C for cold effect	ICD-9: (390–459) ICD-10( I00-I79) CVD
4	Qiao et al. (2015)	22,561/Office of Economic and Statistical Research of the Queensland Treasury	Brisbane, Australia 1996–2004	Daily mean temperature	Time trend, seasonality	0–20	Time-Series	1°C mean temperature increase above the threshold (28°C)	ICD-9 :(390–459) ICD-10(I00-I99) CVD
5	Goggins et al. (2013)	Hong Kong 91/d and Taiwan 33/d/Hong Kong Census and Statistics Bureau, Taiwan’s Department of Health	Hong Kong 19999–2009 Taiwan 1999–2008	Mean temperature	RH, PM10, NO2, SO2f, O3 wind speed, solar radiation, Time trend, seasonality, Day of Week	0–35	Time-Series	10 °C drop in temperature in cold seson for Cold effect	ICD-9 :(390–459) ICD-10(I00-I99)
6	Jun et al., 2015 (Yang et al., 2015c)	1,936,116/Death Register and Report of Chinese CDCg	China 2007–2013 2007–2013	Mean temperature	Not mentioned	0–21	Time-Series	99th compared with MMT for hot effect vs1th compared with MMT for cold effect	ICD-10(I00-I99)
7	Kim et al. (2016)	Ranged from 3.3–50.5 mean daily/Chinese CDC Ministry of Health and Welfare of Japan, and the National Death Registry of Taiwan	30 different cities of East Asia, 1979–2010	Mean Temperature diurnal Temperature rang	PM10, NO2, and SO2	0–21	Time-Series	1°C increase above mean temperature for hot effect	ICD-10(I00-I99)
8	Yang et al. (2012)	Guangzhou Bureau of Health	Guangzhou, China 2003–2007	Maximum, Minimum and Mean temperature	PM10, NO2, and SO2	0–25	Time-Series	99th to the 90th for hot effect	ICD-10(I00-I99) Cardiovascular mortality
9	Guo, Punnasiri & Tong (2012)	11,746/Bureau of Policy and Strategy, Ministry of Public Health, Thailand	Thailand 1999–2008	Maximum, Minimum and Mean Temperature	PM10, O3, RH Influenza	0–21	Time-Series	99th compared to 75th for hot effect vs 1st compared to 25th for cold effect	ICD-10(I00-I99) Cardiovascular mortality
10	Yi & Chan (2015)	98,091/Hong Kong Census and Statistics Department	Hong Kong 2002–2011	Maximum, Minimum and Mean Temperature	PM10, NO2, and SO2	0–21	Time-Series	99th compared to 75th for hot effect vs 1st compared to 25th for cold effect	ICD-10(I00-I99) Cardiovascular mortality
11	Seposo, Dang & Honda (2015)	14.7 mortality per day Philippine Statistics Authority-National Statistics Office (PSA-NSO)	Philippine 2006–2010	Daily average temperature	Seasonal effect	No	Time-Series	1st respective to MMT for cold effect vs. 99th respective to MMT for hot effect	ICD codes (I00–I99) Cardiovascular-related mortality
12	Chen et al. (2014)	126,925/Death Register System from Chinese CDC	China 2009–2011	Maximum, Minimum and Mean Temperature	PM10, NO2, and SO2	0–14	Time-Series	99th compared to 75th for hot effect vs 1st compared to 25th for cold effect	ICD -10: (I00–I25) Coronary artery Disease
13	Guo et al. (2012)	16,559/Chinese CDC	China 2004–2008	Maximum, Minimum and Mean Temperature	PM10, and NO2	0–20	Time-Series	99th compared to 90th for hot effect vs 1st compared to 10th for cold effect	ICD -10: (I00–I25) Coronary artery Disease
14	(Kim et al., 2015)	8.5 mortality per day Statistics Korea, National Institute of Environmental Research	Korea 1995–2011	Daily Mean Temperature	RH, holidays, Day of Week, Time trend, PM10, NO2	0–21	Time-Series	99th compared to 90th for hot effect vs 10th compared to 25th for cold effect	ICD-10 (I20-I59) ICD-9(410–429)
15	(Huang et al., 2015)	552,866/Chinese CDC	China 2006–2011	Mean Temperature	Seasonal trend, Day of Week, RH, Duration of sunshine, Precipitation, Atmospheric Pressure	0–21	Time-Series	1°C increase from 25°C for hot effect, 1°C decrease from 25°C for cold effect	ICD codes (I00–I99)
16	Yang et al. (2015a)	57,806/Central urban district of Shanghai	Shanghai, China 1981-2012	Daily mean Temperature	Seasonality, Day of Week RH, Holidays, population size	0–28	Time-Series	99th compared to 90th for hot effect vs 1st compared to 10th for cold effect	ICD-9: (390–459) ICD codes (I00–I99)
17	Breitner et al. (2014a); Breitner et al. (2014b)	Not mentioned/Bavarian State Office for Statistics and Data Processing	Bavaria, Germany 1990–2007	Mean Temperature	Ozone, PM10 Influenza epidemic, time trend, Day of week, RH, barometric pressure	0–14	Time-Series	99th compared to 90th for hot effect vs 1st compared to 10th for cold effect	ICD-9: (390–459)ICD codes (I00–I99)
18	Wang et al. (2014)	18,530/Suzhou Center for Disease Control and Prevention	Suzhou, China 2005–2008	Mean Temperature	PM10, NO2, and SO2	0–28	Time-Series	99th compared to MMT(26 C) for hot effect vs 1st compared to MMT(26 C) for cold effect	ICD codes (I00–I99)
19	(Ma, Chen & Kan, 2014)	Not mentioned/Municipal Center for Disease Control and Prevention (CDC)	17 large cities of China 1996–2008	Mean Temperature	PM10, NO2, and SO2	0–28	Time-Series	99th compared to 75th for hot effectvs 1st compared to 25th for cold effect	ICD codes (I00–I99)
20	Yang et al. (2015b)	23.8 mortality per day Guangzhou Bureau of Health	Guangzhou, China 2003–2007	Mean Temperature	PM10, NO2, and SO2, Seasonality, RH, Atmospheric Pressure	0–30	Time-Series	99th compared to 75th for hot effect vs 1st compared to 25th for cold effect	ICD codes (I00–I99)
21	Huang, Wang & Yu (2014)	19,418/Chinese CDC	Changsha, China 2008–2011	Daily Mean, Maximum and Minimum temperature	Long-term, Seasonality, barometric pressure, RH	0–30	Time-Series	1°C decrease from 10°C for cold effect vs 1°C increase from 29°C for hot effect	ICD codes (I00–I79)
22	Yu et al. (2011a)	22,805/Office of Economic and Statistical Research of the Statistical Research of the Queensland Treasury	Brisbane, Australia 1996–2004	Maximum, Minimum Temperature	PM10, NO2, O3	0–31	Time-Series	1°C increase from 24°C for cold effect vs 1°C decrease from 24°C for cold effect	ICD-9: (390–499)ICD codes (I00–I99)
23	Chan et al. (2012)	129,688/Hong Kong Census And Statistics Department	Hong Kong 1998–2006	Average daily mean temperature	PM10, NO2, SO2, O3 Day of the week and holiday	0–14	Time-Series	1°C increase from 28.2°C for hot effect	ICD9, 390–459 ICD 10 I00–I99
24	Wang et al. (2015)	Not mentioned/Chinese CDC	Chinese 2007–2009	Mean Temperature	Seasonality, Time trend, PM10, NO2, SO2, RH, Wind speed	0–27	Time-Series	99th compared to 90th for hot effect vs 1st compared to 10th for cold effect	ICD 10 I00–I99
25	Bai, Woodward & Liu (2014)	5,610/Tibetan CDC	China 2008–2012	Mean Temperature	–	0–14	Time-Series	99th compared to 75th for hot effect vs 1st compared to 10th for cold effect	ICD 10 I00–I99
26	Gomez-Acebo, Llorca & Dierssen (2013)	1,252/Spanish National Institute for Statistical	Spain 2004–2005	Minimum temperatures	Age, sex, underlying disease	0–6	Case-crossover	5th (−13.8 C) compared with over the 5th (1.8 C) for cold effect	ICD-10 I00–I99

Notes.

^aPM: Particle Matter.

^bNOx: Nitrous Oxide.

^cO₃: Ozone.

^dICD: International Classification of Diseases.

^eRH: Relative Humidity.

^fSO₂: Sulfur Dioxide.

^gCDC: Center of control Diseases.