| Literature DB >> 31070715 |
Emily Y Y Chan1,2,3,4, Janice Y Ho2, Heidi H Y Hung2, Sida Liu2, Holly C Y Lam1.
Abstract
BACKGROUND: This review examines the human health impact of climate change in China. Through reviewing available research findings under four major climate change phenomena, namely extreme temperature, altered rainfall pattern, rise of sea level and extreme weather events, relevant implications for other middle-income population with similar contexts will be synthesized. SOURCES OF DATA: Sources of data included bilingual peer-reviewed articles published between 2000 and 2018 in PubMed, Google Scholar and China Academic Journals Full-text Database. AREAS OF AGREEMENT: The impact of temperature on mortality outcomes was the most extensively studied, with the strongest cause-specific mortality risks between temperature and cardiovascular and respiratory mortality. The geographical focuses of the studies indicated variations in health risks and impacts of different climate change phenomena across the country. AREAS OF CONTROVERSY: While rainfall-related studies predominantly focus on its impact on infectious and vector-borne diseases, consistent associations were not often found. GROWING POINTS: Mental health outcomes of climate change had been gaining increasing attention, particularly in the context of extreme weather events. The number of projection studies on the long-term impact had been growing. AREAS TIMELY FOR DEVELOPING RESEARCH: The lack of studies on the health implications of rising sea levels and on comorbidity and injury outcomes warrants immediate attention. Evidence is needed to understand health impacts on vulnerable populations living in growing urbanized cities and urban enclaves, in particular migrant workers. Location-specific climate-health outcome thresholds (such as temperature-mortality threshold) will be needed to support evidence-based clinical management plans and health impact mitigation strategies to protect vulnerable communities.Entities:
Keywords: China; climate change; extreme weather; health impact; temperature; urban
Mesh:
Year: 2019 PMID: 31070715 PMCID: PMC6587073 DOI: 10.1093/bmb/ldz011
Source DB: PubMed Journal: Br Med Bull ISSN: 0007-1420 Impact factor: 4.291
Fig. 1Framework on climate change and its impact on health (adapted from Watts et al., 2015)
Length of study datasets found in this review
| Years | 1–5 | 6–10 | 11–20 | 21+ | Projection studies | Total |
|---|---|---|---|---|---|---|
| Number of articles | 102 | 55 | 16 | 14 | 9 | 196 |
Geographical distribution of studies included in this review, by study exposure and outcome types
| Exposure types | Outcome types | |||||||||||
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| Location | Total | Temperature | Meteorological | Flood | Typhoon | Mortality | Vector-borne | Other infectious diseases | Mental health | Preterm birth | Other morbidities | |
| Across China (multiple locations) | 33 | 21 | 12 | 0 | 0 | 17 | 7 | 7 | 0 | 1 | 1 | |
| North China |
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| Beijing | 16 | 8 | 8 | 0 | 0 | 7 | 0 | 7 | 0 | 0 | 2 | |
| Tianjin | 3 | 2 | 1 | 0 | 0 | 2 | 0 | 1 | 0 | 0 | 0 | |
| Hebei | 2 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | |
| Shanxi | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Inner Mongolia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Northeast China |
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| Heilongjiang | 3 | 3 | 0 | 0 | 0 | 3 | 0 | 0 | 0 | 0 | 0 | |
| Jilin | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Liaoning | 4 | 0 | 3 | 1 | 0 | 0 | 0 | 2 | 0 | 0 | 2 | |
| East China |
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| Shanghai | 11 | 10 | 1 | 0 | 0 | 5 | 0 | 3 | 0 | 0 | 3 | |
| Anhui | 20 | 5 | 11 | 4 | 0 | 1 | 8 | 10 | 0 | 0 | 1 | |
| Fujian | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 1 | |
| Jiangsu | 7 | 4 | 2 | 1 | 0 | 4 | 1 | 2 | 0 | 0 | 0 | |
| Jiangxi | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Shandong | 13 | 4 | 7 | 2 | 0 | 3 | 2 | 7 | 0 | 0 | 1 | |
| Zhejiang | 5 | 2 | 1 | 0 | 2 | 1 | 0 | 2 | 0 | 0 | 2 | |
| South China |
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| Guangdong | 29 | 12 | 13 | 0 | 4 | 10 | 10 | 7 | 0 | 1 | 1 | |
| Guangxi | 2 | 0 | 0 | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 0 | |
| Hainan | 2 | 0 | 0 | 0 | 2 | 0 | 0 | 1 | 1 | 0 | 0 | |
| Hong Kong SAR | 14 | 7 | 6 | 0 | 1 | 4 | 0 | 5 | 0 | 0 | 5 | |
| Macau SAR | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Central China |
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| Henan | 6 | 0 | 3 | 3 | 0 | 0 | 3 | 3 | 0 | 0 | 0 | |
| Hubei | 7 | 3 | 2 | 2 | 0 | 3 | 1 | 3 | 0 | 0 | 0 | |
| Hunan | 11 | 0 | 0 | 11 | 0 | 1 | 0 | 2 | 8 | 0 | 0 | |
| Southwest China |
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| Chongqing | 8 | 4 | 4 | 0 | 0 | 2 | 3 | 1 | 0 | 0 | 2 | |
| Sichuan | 3 | 1 | 2 | 0 | 0 | 1 | 2 | 0 | 0 | 0 | 0 | |
| Guizhou | 3 | 0 | 3 | 0 | 0 | 0 | 2 | 1 | 0 | 0 | 0 | |
| Yunnan | 6 | 1 | 5 | 0 | 0 | 1 | 5 | 0 | 0 | 0 | 0 | |
| Tibet | 3 | 2 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 1 | |
| Northwest China |
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| Shaanxi | 2 | 0 | 1 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | |
| Gansu | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | |
| Qinghai | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | |
| Ningxia | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Xinjiang | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | |
| Total | 217 | 90 | 89 | 26 | 11 | 66 | 46 | 71 | 9 | 2 | 23 | |
*Indicates China’s municipalities and special administrative regions that are not under any province.
†Some cross-provincial studies were double-counted.