| Literature DB >> 29684649 |
Michael Xiaoliang Tong1, Alana Hansen2, Scott Hanson-Easey3, Jianjun Xiang4, Scott Cameron5, Qiyong Liu6, Xiaobo Liu7, Yehuan Sun8, Philip Weinstein9, Gil-Soo Han10, Peng Bi11.
Abstract
OBJECTIVES: Infectious diseases are a major cause of morbidity and mortality in China. The capacity of hospitals to deal with the challenge from emerging and re-emerging infectious diseases due to climate change is of great importance to population health. This study aimed to explore the capacity of hospitals in China to deal with such challenges.Entities:
Keywords: China; Climate change; Clinical professionals; Hospital capacity; Infectious diseases
Mesh:
Year: 2018 PMID: 29684649 PMCID: PMC7116943 DOI: 10.1016/j.socscimed.2018.04.021
Source DB: PubMed Journal: Soc Sci Med ISSN: 0277-9536 Impact factor: 4.634
Fig. 1Geographical location of study site - Anhui Province in China.
Demographic characteristics of the clinical professionals in Anhui, China (N = 611).
| Demographic variables | Number | Percent (%) |
|---|---|---|
| 20-29 | 390 | 64.6 |
| ≥30 | 214 | 35.4 |
| Male | 179 | 29.9 |
| Female | 419 | 70.1 |
| Junior | 268 | 43.9 |
| Intermediate | 121 | 19.8 |
| Senior | 28 | 4.6 |
| Other | 194 | 31.7 |
| <5 | 322 | 56.2 |
| 5-9 | 107 | 18.7 |
| ≥10 | 144 | 25.1 |
| Below undergraduate | 239 | 39.5 |
| Undergraduate degree or above | 366 | 60.5 |
| Doctor | 269 | 44.0 |
| Nurse | 283 | 46.3 |
| Other | 59 | 9.7 |
The total number may not be equal to 611 as some questions were not answered or classified.
Other professional level was those who had not been assessed in professional classification; other occupations included public health officer, lab technician and administrator.
Perceptions of climate change and its impacts among clinical professionals in Anhui, China.a.
| Opinions on climate change items | Number | Percent (%) |
|---|---|---|
| The concern of climate change | ||
| Very concerned | 124 | 20.4 |
| Concerned | 337 | 55.3 |
| Slightly concerned | 138 | 22.7 |
| Not concerned | 10 | 1.6 |
| The area is becoming warmer | ||
| Yes | 434 | 71.0 |
| No | 84 | 13.8 |
| Unsure | 93 | 15.2 |
| Climate change will have a negative effect on population health | ||
| Yes | 584 | 95.6 |
| No | 11 | 1.8 |
| Unsure | 16 | 2.6 |
| Predicted increasing temperatures will influence the transmission of infectious diseases | ||
| Yes | 580 | 94.9 |
| No | 13 | 2.1 |
| Unsure | 18 | 3.0 |
| Predicted changes in precipitation patterns will influence the transmission of infectious diseases | ||
| Yes | 558 | 91.3 |
| No | 24 | 3.9 |
| Unsure | 29 | 4.8 |
| The association between climate change and malaria | ||
| Extremely likely | 242 | 39.6 |
| Very likely | 270 | 44.2 |
| Somewhat likely | 52 | 8.5 |
| Not likely or Unsure | 47 | 7.7 |
| The association between climate change and dengue | ||
| Extremely likely | 192 | 31.4 |
| Very likely | 262 | 42.9 |
| Somewhat likely | 82 | 13.4 |
| Not likely or Unsure | 75 | 12.3 |
| The association between climate change and HFRS | ||
| Extremely likely | 168 | 27.5 |
| Very likely | 259 | 42.4 |
| Somewhat likely | 110 | 18.0 |
| Not likely or Unsure | 74 | 12.1 |
Data in this table are frequency and percentage N (%).
Perceptions of malaria, dengue and HFRS among clinical professionals in Anhui, Chinaa.
| The trend of patients with these infectious diseases over the past 10 years | ||||
|---|---|---|---|---|
| Increased N (%) | Decreased N (%) | Not a big change N (%) | Unsure N (%) | |
| Malaria | 137 (22.4%) | 78 (12.8%) | 171 (28.0%) | 225 (36.8%) |
| Dengue | 124 (20.3%) | 86 (14.1%) | 83 (13.6%) | 318 (52.0%) |
| HFRS | 138 (22.6%) | 84 (13.7) | 150 (24.6%) | 239 (39.1%) |
Data in this table are frequency and percentage N (%). Abbreviation: HFRS, Hemorrhagic Fever with Renal Syndrome; CDC, Centers for Disease Control and Prevention.
Perceptions of hospital capacity to deal with infectious diseases in Anhui, China.a.
| Questions concerning capacity to deal with infectious diseases | Agree strongly N (%) | Agree somewhat N (%) | Disagree somewhat N (%) | Disagree strongly N (%) | Unsure N (%) |
|---|---|---|---|---|---|
| Current numbers of staff at this hospital will be adequate in the event of major disease outbreaks | 316 (52.4) | 259 (42.9) | 16 (2.7) | 1 (0.2) | 11 (1.8) |
| Staff are kept up to date and well informed about current infectious disease trends | 252 (42.1) | 300 (50.2) | 27 (4.5) | 3 (0.5) | 16 (2.7) |
| The quality of reported data is excellent | 277 (46.2) | 291 (48.5) | 17 (2.8) | 1 (0.2) | 14 (2.3) |
| Logistical support in this hospital needs to be strengthened | 344 (57.2) | 236 (39.3) | 11 (1.8) | 1 (0.2) | 9 (1.5) |
| More research needs to be done on the health impacts of climate change | 295 (49.3) | 276 (46.2) | 18 (3.0) | 1 (0.2) | 8 (1.3) |
| This hospital is well prepared to respond to the threat of a serious emerging disease | 283 (47.0) | 284 (47.2) | 19 (3.2) | 2 (0.3) | 14 (2.3) |
Data in this table are frequency and percentage N (%).
Perceptions of strategies to build capacity to curb the health impact of climate change on infectious diseases in Anhui, China.a.
| The importance of these strategies to curb the health impact of climate change on infectious diseases | Extremely important N (%) | Very important N (%) | Important N (%) | Less important N (%) | Not important N (%) |
|---|---|---|---|---|---|
| Prevention and control measures | 458 (75.9) | 138 (22.9) | 6 (1.0) | 1 (0.2) | 0 |
| Better response mechanisms when outbreaks occur | 405 (67.4) | 184 (30.6) | 12 (2.0) | 0 | 0 |
| Strengthening the monitoring of infectious diseases | 415 (68.8) | 173 (28.7) | 15 (2.5) | 0 | 0 |
| The ability to actively forecast disease outbreaks by early warning systems | 406 (67.3) | 174 (28.9) | 22 (3.6) | 1 (0.2) | 0 |
| More collaboration with CDC to deal with infectious disease outbreak | 443 (73.7) | 143 (23.8) | 15 (2.5) | 0 | 0 |
| More affordable access to health care for the population | 362 (60.5) | 206 (34.5) | 29 (4.8) | 1 (0.2) | 0 |
| More health education programs | 398 (66.7) | 180 (30.1) | 18 (3.0) | 1 (0.2) | 0 |
| Increase laboratory diagnostic ability in rural hospitals | 391 (65.1) | 189 (31.4) | 21 (3.5) | 0 | 0 |
| Improve accessibility of online infectious disease reporting for rural hospitals | 423 (70.5) | 154 (25.7) | 21 (3.5) | 2 (0.3) | 0 |
| More funding for rural health care | 425 (70.7) | 156 (25.9) | 19 (3.2) | 1 (0.2) | 0 |
Data in this table are frequency and percentage N (%). Abbreviation: CDC, Centers for Disease Control and Prevention.