Effective interventions on health effects of Chinese rural elderly under heat exposure

doi:10.1007/s11783-022-1545-4

Front. Environ. Sci. Eng.

2022, Vol. 16

Issue (5) : 66 https://doi.org/10.1007/s11783-022-1545-4

RESEARCH ARTICLE

Effective interventions on health effects of Chinese rural elderly under heat exposure

Yujia Huang¹, Ting Zhang¹, Jianing Lou¹, Peng Wang², Lei Huang¹(

)

¹. State Key Laboratory of Pollution Control and Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China
². Faculty of Civil Engineering and Mechanics, Jiangsu University, Zhenjiang 212013, China

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Abstract

● Education and subsidy were effective interventions during short-term heat exposure.

● A new index was defined to evaluate the intervention performance.

● Blood pressure and sleep duration were more heat-sensitive for the elderly.

Due to climate change, the heatwave has become a more serious public health threat with aging as an aggravating factor in recent years. There is a pressing need to detect the most effective prevention and response measures. However, the specific health effects of interventions have not been characterized on an individual scale. In this study, an intervention experiment was designed to explore the health effects of heat exposure at the individual level and assess the effects of different interventions based on a comprehensive health sensitivity index (CHSI) in Xinyi, China. Forty-one subjects were recruited randomly, and divided into one control group and three intervention groups. Interventions included education (Educate by lecturing, offering relative materials, and communication), subsidy support (offer subsidy to offset the cost of running air conditioning), and cooling-spray (install a piece of cooling-spray equipment in the yard). Results showed that systolic blood pressure (SBP) and deep sleep duration (DSD) were significantly affected by short-term heat exposure, and the effects could be alleviated by three types of interventions. The estimated CHSI indicated that the effective days of the education group were longer than other groups, while the lower CHSI of the subsidy group showed lower sensitivity than the control group. These findings provide feasible implementation strategies to optimize Heat-health action plans and evaluate the intervention performance.

Keywords High temperature Health effect Comprehensive evaluation Intervention Rural elderly

Corresponding Author(s): Lei Huang

Issue Date: 01 June 2022

Cite this article:

Yujia Huang,Ting Zhang,Jianing Lou, et al. Effective interventions on health effects of Chinese rural elderly under heat exposure[J]. Front. Environ. Sci. Eng., 2022, 16(5): 66.

URL:

https://academic.hep.com.cn/fese/EN/10.1007/s11783-022-1545-4
https://academic.hep.com.cn/fese/EN/Y2022/V16/I5/66

Fig.1 Flowchart of the methodology.

Tab.1 Baseline characteristics of the study population by groups

Fig.2 Changes in different health metrics of control group from baseline (period 0) (*P < 0.05, ** P < 0.01, *** P < 0.001, compared with period 0, the error bars represent estimate value ± SE).

Fig.3 Changes in blood pressure of three intervention groups from baseline (period 0) (*P < 0.05, compared with period 0, the error bars represent estimate value ± SE).

Fig.4 Difference in blood pressure between intervention groups and control group (*P < 0.05, ** P < 0.01, compared with control group, the error bars represent estimate value ± SE).

Fig.5 Changes in heart rate of three intervention groups from baseline (period 0) (The error bars represent estimate value ± SE).

Fig.6 Changes in sleep duration of three intervention groups from baseline (period 0) (the error bars represent estimate value ±SE).

Tab.2 Weights of health metrics

Fig.7 Variations in comprehensive sensitivity Index in four groups along with intervention time (*P < 0.05, ** P < 0.01, compared with the control group, the error bars represent estimate value ± SE).

1	N Aghamohammadi , C S Fong , M H M Idrus , L Ramakreshnan , N M Sulaiman . (2021). Environmental heat-related health symptoms among community in a tropical city. Science of the Total Environment, 782 : 146611 https://doi.org/10.1016/j.scitotenv.2021.146611
2	G B Anderson , M L Bell . (2011). Heat waves in the United States: mortality risk during heat waves and effect modification by heat wave characteristics in 43 U. S. communities. Environmental Health Perspectives, 119( 2): 210– 218 https://doi.org/10.1289/ehp.1002313
3	J Ban , W Shi , L Cui , X Liu , C Jiang , L Han , R Wang , T Li . (2019). Health-risk perception and its mediating effect on protective behavioral adaptation to heat waves. Environmental Research, 172 : 27– 33 https://doi.org/10.1016/j.envres.2019.01.006
4	S Bose-O’Reilly , H Daanen , K Deering , N Gerrett , M M T E Huynen , J Lee , S Karrasch , F Matthies-Wiesler , H Mertes , J Schoierer . et al.. (2021). COVID-19 and heat waves: New challenges for healthcare systems. Environmental Research, 198 : 111153 https://doi.org/10.1016/j.envres.2021.111153
5	M Brennan , P M O’Shea , E C Mulkerrin . (2020). Preventative strategies and interventions to improve outcomes during heatwaves. Age and Ageing, 49( 5): 729– 732 https://doi.org/10.1093/ageing/afaa125
6	W Cai , C Zhang , H P Suen , S Ai , Y Bai , J Bao , B Chen , L Cheng , X Cui , H Dai . et al.. (2021). The 2020 China report of the Lancet Countdown on health and climate change. Lancet Public Health, 6( 1): e64– e81 https://doi.org/10.1016/S2468-2667(20)30256-5
7	A E Carrillo A D Flouris C L Herry S R Notley M J Macartney A J E Seely H E Wright Beatty G P Kenny ( 2019). Age-related reductions in heart rate variability do not worsen during exposure to humid compared to dry heat: A secondary analysis. Temperature (Austin, Tex.), 6( 4): 341− 345 pmid: 31934605
8	K Chen , L Huang , L Zhou , Z Ma , J Bi , T Li . (2015). Spatial analysis of the effect of the 2010 heat wave on stroke mortality in Nanjing, China. Scientific Reports, 5( 1): 10816 https://doi.org/10.1038/srep10816
9	K Chen , L Zhou , X Chen , Z Ma , Y Liu , L Huang , J Bi , P L Kinney . (2016). Urbanization level and vulnerability to heat-related mortality in Jiangsu Province, China. Environmental Health Perspectives, 124( 12): 1863– 1869 https://doi.org/10.1289/EHP204
10	Q Chen , M Ding , X Yang , K Hu . (2017). Spatially explicit assessment of heat health risks using multi-source data: A case study of the Yangtze River Delta Region, China. Journal of Geo-Information Science, 19( 11): 1475– 1484
11	R Chen , P Yin , L Wang , C Liu , Y Niu , W Wang , Y Jiang , Y Liu , J Liu , J Qi . et al.. (2018). Association between ambient temperature and mortality risk and burden: Time series study in 272 main Chinese cities. BMJ (Clinical Research Ed.), 363 : k4306 https://doi.org/10.1136/bmj.k4306
12	J Cheng , Z Xu , H Bambrick , H Su , S Tong , W Hu . (2018). Heatwave and elderly mortality: An evaluation of death burden and health costs considering short-term mortality displacement. Environment International, 115 : 334– 342 https://doi.org/10.1016/j.envint.2018.03.041
13	Y Deng , B Cao , B Liu , Y Zhu . (2020). Effects of local heating on thermal comfort of standing people in extremely cold environments. Building and Environment, 185 : 107256 https://doi.org/10.1016/j.buildenv.2020.107256
14	K L Ebi . (2019). Effective heat action plans: research to interventions. Environmental Research Letters, 14 : 122001 https://doi.org/10.1088/1748-9326/ab5ab0
15	A Fouillet , G Rey , F Laurent , G Pavillon , S Bellec , C Guihenneuc-Jouyaux , J Clavel , E Jougla , D Hémon . (2006). Excess mortality related to the August 2003 heat wave in France. International Archives of Occupational and Environmental Health, 80( 1): 16– 24 https://doi.org/10.1007/s00420-006-0089-4
16	A Gasparrini B Armstrong ( 2011). The impact of heat waves on mortality. Epidemiology (Cambridge, Mass.), 22( 1): 68−73 pmid: 21150355
17	P Guo M Tang ( 2015). The effect of high temperature on human cardiovascular system and blood sugar. Chinese Journal of Convalescent Medicine, 24(2): 176− 178 (in Chinese)
18	Y Guo , A Gasparrini , B G Armstrong , B Tawatsupa , A Tobias , E Lavigne , M S Z S Coelho , X Pan , H Kim , M Hashizume . et al.. (2017). Heat wave and mortality: A multicountry, multicommunity study. Environmental Health Perspectives, 125( 8): 087006 https://doi.org/10.1289/EHP1026
19	J Han , C Miao , Q Duan , J Wu , X Lei , W Liao . (2020). Variations in start date, end date, frequency and intensity of yearly temperature extremes across China during the period 1961–2017. Environmental Research Letters, 15( 4): 045007 https://doi.org/10.1088/1748-9326/ab7390
20	M Hendel K Azos-Diaz B Tremeac ( 2016). Behavioral adaptation to heat-related health risks in cities. Energy and Buildings: S0378778816317212
21	L Huang , Y Huang , P Liu , G Wang , J Bi . (2020). Research on regional comprehensive environmental risk assessment method system. China Environmental Science, 40( 12): 5468– 5474
22	L Huang , Q Yang , J Li , J Chen , R He , C Zhang , K Chen , S G Dong , Y Liu . (2018). Risk perception of heat waves and its spatial variation in Nanjing, China. International Journal of Biometeorology, 62( 5): 783– 794 https://doi.org/10.1007/s00484-017-1480-4
23	O Jay , A Capon , P Berry , C Broderick , Dear R de , G Havenith , Y Honda , R S Kovats , W Ma , A Malik . et al.. (2021). Reducing the health effects of hot weather and heat extremes: From personal cooling strategies to green cities. Lancet, 398( 10301): 709– 724 https://doi.org/10.1016/S0140-6736(21)01209-5
24	R Kaiser A Le Tertre J Schwartz C A Gotway W R Daley C H Rubin ( 2007). The effect of the 1995 heat wave in Chicago on all-cause and cause-specific mortality. American Journal of Public Health, 97( Suppl 1): S158− S162 pmid: 17413056
25	Y M Kim , S Kim , H K Cheong , B Ahn , K Choi . (2012). Effects of heat wave on body temperature and blood pressure in the poor and elderly. Environmental Health and Toxicology, 27 : e2012013 https://doi.org/10.5620/eht.2012.27.e2012013
26	J Li , X Xu , J Wang , Y Zhao , X P Song , Z D Liu , L N Cao , B F Jiang , Q Y Liu . (2016a). Analysis of a community-based intervention to reduce heat-related illness during heat waves in Licheng, China: A Quasi-experimental study. Biomed Environmental Science, 29( 11): 802– 813
27	Q Li H Su Y Shi L Wang D Wu ( 2016b). Temporal-spatial change characteristics of summer heatwaves in Jiangsu-Zhejiang-Shanghai region during 1961–2010. Resources and Environment in the Yangtze Basin, 25( 3): 506− 513 (in Chinese)
28	J Lou , J Ban , T Zhang , P Wang , Y Wu , L Huang , T Li , J Bi . (2021). An intervention study of the rural elderly for improving exposure, risk perception and behavioral responses under high temperature. Environmental Research Letters, 16( 5): 055029 https://doi.org/10.1088/1748-9326/abfa4f
29	P Lu , Q Zhao , G Xia , R Xu , L Hanna , J Jiang , S Li , Y Guo . (2021). Temporal trends of the association between ambient temperature and cardiovascular mortality: A 17-year case-crossover study. Environmental Research Letters, 16( 4): 045004 https://doi.org/10.1088/1748-9326/abab33
30	L Madaniyazi , Y Zhou , S Li , G Williams , J J Jaakkola , X Liang , Y Liu , S Wu , Y Guo . (2016). Outdoor temperature, heart rate and blood pressure in Chinese adults: Effect modification by individual characteristics. Scientific Reports, 6( 1): 21003 https://doi.org/10.1038/srep21003
31	G S Martinez , C Linares , A Ayuso , V Kendrovski , M Boeckmann , J Diaz . (2019). Heat-health action plans in Europe: Challenges ahead and how to tackle them. Environmental Research, 176 : 108548 https://doi.org/10.1016/j.envres.2019.108548
32	G Masato , A Bone , A Charlton-Perez , S Cavany , R Neal , R Dankers , H Dacre , K Carmichael , V Murray . (2015). Improving the health forecasting alert system for cold weather and heat-waves in england: A proof-of-concept using temperature-mortality relationships. PLoS One, 10( 10): e0137804 https://doi.org/10.1371/journal.pone.0137804
33	A Matzarakis , G Laschewski , S Muthers . (2020). The heat health warning system in germany: Application and warnings for 2005 to 2019. Atmosphere, 11( 2): 170 https://doi.org/10.3390/atmos11020170
34	E A S Mayrhuber , M L A Duckers , P Wallner , A Arnberger , B Allex , L Wiesboeck , A Wanka , F Kolland , R Eder , H P Hutter . et al.. (2018). Vulnerability to heatwaves and implications for public health interventions: A scoping review. Environmental Research, 166 : 42– 54 https://doi.org/10.1016/j.envres.2018.05.021
35	R D Meade , A P Akerman , S R Notley , R McGinn , P Poirier , P Gosselin , G P Kenny . (2020). Physiological factors characterizing heat-vulnerable older adults: A narrative review. Environment International, 144 : 105909 https://doi.org/10.1016/j.envint.2020.105909
36	L Morais , A Lopes , P Nogueira . (2021). Human health outcomes at the neighbourhood scale implications: Elderly’s heat-related cardiorespiratory mortality and its influencing factors. Science of the Total Environment, 760 : 144036 https://doi.org/10.1016/j.scitotenv.2020.144036
37	P Nogueira , E Paixao , J Falcao . (2005). Behaviour of the Portuguese population during hot seasons and the heat wave of august 2003. Revista Portuguesa de Saude Publica, 23( 2): 3– 18
38	E Pennisi ( 2020). Living with heat. Science (New York, N.Y.), 370( 6518): 778− 781 pmid: 33184201
39	C Qu J Bi L Huang F Li J Yang ( 2010). Dynamic comprehensive evaluation on regional environmental risk. Acta Scicentific Natural of Universities Pekinesis, 46( 3): 477− 482 (in Chinese)
40	S C Sheridan . (2007). A survey of public perception and response to heat warnings across four North American cities: An evaluation of municipal effectiveness. International Journal of Biometeorology, 52( 1): 3– 15 https://doi.org/10.1007/s00484-006-0052-9
41	X Song , Z Zhang , Y Chen , P Wang , M Xiang , P Shi , F Tao . (2014). Spatiotemporal changes of global extreme temperature events (ETEs) since 1981 and the meteorological causes. Natural Hazards, 70( 2): 975– 994 https://doi.org/10.1007/s11069-013-0856-y
42	N Takahashi , R Nakao , K Ueda , M Ono , M Kondo , Y Honda , M Hashizume . (2015). Community trial on heat related-illness prevention behaviors and knowledge for the elderly. International Journal of Environmental Research and Public Health, 12( 3): 3188– 3214 https://doi.org/10.3390/ijerph120303188
43	K Tamura K Uchida T Ishigami ( 2021). An interesting link between quality of sleep and a measure of blood pressure variability. Journal of Clinical Hypertension (Greenwich, Conn.), 23( 2): 331− 333 pmid: 33373081
44	J Tan Y Zheng ( 2013). Temporal and spatial distribution characteristics of heat waves in main capital cities of China. Meteorological Science and Technology, 41( 2): 347− 351 (in Chinese)
45	T W Tsang , K W Mui , L T Wong . (2021). Investigation of thermal comfort in sleeping environment and its association with sleep quality. Building and Environment, 187 : 107406 https://doi.org/10.1016/j.buildenv.2020.107406
46	J Wang B Meng T Pei Y Du J Zhang S Chen B Tian G Zhi ( 2021). Mapping the exposure and sensitivity to heat wave events in China’s megacities. Science of the Total Environment, 755( Pt 1): 142734 pmid: 33348486
47	Q Q Wang , Y Yu , Y H Li , Z Ding , X D Chen . (2018). Evaluation the impact of community intervention on heat wave in Nanjing, China. Chinese Journal of Preventive Medicine, 52( 2): 188– 190
48	N Watts , M Amann , N Arnell , S Ayeb-Karlsson , J Beagley , K Belesova , M Boykoff , P Byass , W Cai , D Campbell-Lendrum . et al.. (2021). The 2020 report of The Lancet Countdown on health and climate change: responding to converging crises. Lancet, 397( 10269): 129– 170 https://doi.org/10.1016/S0140-6736(20)32290-X
49	N Watts , M Amann , N Arnell , S Ayeb-Karlsson , K Belesova , M Boykoff , P Byass , W Cai , D Campbell-Lendrum , S Capstick . et al.. (2019). The 2019 report of the Lancet Countdown on health and climate change: Ensuring that the health of a child born today is not defined by a changing climate. Lancet, 394( 10211): 1836– 1878 https://doi.org/10.1016/S0140-6736(19)32596-6
50	J Wei , Y Mi , Y Li , B Xin , Y Wang . (2021). Factors associated with awareness, treatment and control of hypertension among 3579 hypertensive adults in China: Data from the China health and nutrition survey. BMC Public Health, 21( 1): 423 https://doi.org/10.1186/s12889-021-10417-4
51	Y Wu J Lou T Li Q Wang J Ban X Sun L Huang ( 2020). Analysis of individual difference in rural vulnerable groups’ risk perception of heat waves and their protective behavior. Journal of Environmental Hygiene, 10(1): 25− 30 (in Chinese)
52	J Yang , M Zhou , Z Ren , M Li , B Wang , L Liu , C Q Ou , P Yin , J Sun , S Tong , H Wang , C Zhang , J Wang , Y Guo , Q Liu . (2021). Projecting heat-related excess mortality under climate change scenarios in China. Nature Communications, 12( 1): 1039 https://doi.org/10.1038/s41467-021-21305-1
53	D Ye , J Yin , Z Chen , Y Zheng , R Wu . (2013). Spatiotemporal change characteristics of summer heatwaves in China in 1961–2010. Progressus Inquisitiones de Mutatione Climatis, 9( 1): 15– 20
54	A Zhang , W Hu , J Li , R Wei , J Lin , W Ma . (2019a). Impact of heatwaves on daily outpatient visits of respiratory disease: A time-stratified case-crossover study. Environmental Research, 169 : 196– 205 https://doi.org/10.1016/j.envres.2018.10.034
55	N Zhang , B Cao , Y Zhu . (2019b). Effects of pre-sleep thermal environment on human thermal state and sleep quality. Building and Environment, 148 : 600– 608 https://doi.org/10.1016/j.buildenv.2018.11.035
56	N Zhang , B Cao , Y X Zhu . (2018). Indoor environment and sleep quality: A research based on online survey and field study. Building and Environment, 137 : 198– 207 https://doi.org/10.1016/j.buildenv.2018.04.007
57	Y Zheng X Ding R Wu J Yin( 2012). Temporal and spatial feature analyses of summer high temperature and heat wave in Jiangsu Province in past 50 years. Jounal of Natural Disasters, 21( 2): 43− 50 (in Chinese)
58	L Zhou K Chen X Chen Y Jing Z Ma J Bi P L Kinney( 2017). Heat and mortality for ischemic and hemorrhagic stroke in 12 cities of Jiangsu Province, China. Science of the Total Environment, 601−602: 271− 277 pmid: 28558275
59	X Zhou J Liu ( 2018). High-temperature Operation on the blood pressure and blood lipids of workers in Iron and Steel Enterprises of Urumqi. Industrial Health and Occupational Diseases, 44( 1): 18− 20, 23 (in Chinese)

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