| Literature DB >> 25285440 |
Junni Wei1, Alana Hansen2, Ying Zhang3, Hong Li4, Qiyong Liu5, Yehuan Sun6, Shulian Xue1, Shufang Zhao1, Peng Bi2.
Abstract
There have been increasing concerns about the challenge of emerging and re-emerging infectious diseases due to climate change, especially in developing countries including China. Health professionals play a significant role in the battle to control and prevent infectious diseases. This study therefore aims to investigate the perceptions and attitudes of health professionals at the Centers for Disease Control and Prevention (CDC) in different levels in China, and to consider adaptation measures to deal with the challenge of climate change. In 2013, a cross-sectional questionnaire survey was undertaken among 314 staff in CDCs in Shanxi Province, China, whose routine work involves disease control and prevention. Data were analyzed using descriptive methods and logistic regression. A majority of the CDC staff were aware of the health risks from climate change, especially its impacts on infectious disease transmission in their jurisdictions, and believed climate change might bring about both temporal and spatial change in transmission patterns. It was thought that adaptation measures should be established including: strengthening/improving currently existing disease surveillance systems and vector monitoring; building CDC capacity in terms of infrastructure and in-house health professional training; development and refinement of relevant legislation, policies and guidelines; better coordination among various government departments; the involvement of the community in infectious disease interventions; and collaborative research with other institutions. This study provides a snapshot of the understanding of CDC staff regarding climate change risks relevant to infectious diseases and adaptation in China. Results may help inform future efforts to develop adaptation measures to minimize infectious disease risks due to climate change.Entities:
Mesh:
Year: 2014 PMID: 25285440 PMCID: PMC4186885 DOI: 10.1371/journal.pone.0109476
Source DB: PubMed Journal: PLoS One ISSN: 1932-6203 Impact factor: 3.240
Demographic characteristics of the participants (N = 314).
| Characteristics | Number | Percent (%) | |
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| |||
| 20–39 | 146 | 46.5 | |
| ≥40 | 168 | 53.5 | |
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| Male | 124 | 39.5 | |
| Female | 190 | 60.5 | |
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| Below undergraduate | 95 | 30.3 | |
| At or above undergraduate level | 219 | 69.7 | |
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| Provincial | 59 | 18.8 | |
| Prefecture-level city | 210 | 66.9 | |
| District/county | 45 | 14.3 | |
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| ≤9 | 160 | 51.0 | |
| 10–19 | 79 | 25.2 | |
| 20–39 | 75 | 23.9 | |
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| Junior | 127 | 40.4 | |
| Intermediate | 127 | 40.4 | |
| Senior | 60 | 19.1 | |
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| No | 211 | 67.2 | |
| Yes | 103 | 32.8 | |
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| Infectious Disease control | 247 | 78.9 | |
| Public health | 52 | 16.6 | |
| Medical laboratory | 101 | 32.3 | |
| Emergency response and management | 44 | 14.1 | |
Multiple response was analyzed by using multiple dichotomy method to form a multiple response set; percent of cases column is the percentage of valid cases represented by each category, and these percentages will sum to more than 100% if at least one person made more than one response.
CDC staff's perception about the health impacts from climate change (N = 314).
| Potential health impacts of climate change | Number | Percent (%) | |
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| Infants and children | 214 | 68.2 | |
| Young adults | 13 | 4.1 | |
| Middle-aged | 22 | 7.0 | |
| The elderly | 256 | 81.5 | |
| Lower SES | 61 | 19.4 | |
| Outdoor workers | 196 | 62.4 | |
| People with existing diseases | 207 | 65.9 | |
| Others | 4 | 1.3 | |
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| Respiratory diseases (e.g. asthma, pneumonia, chronic block pulmonary emphysema) | 249 | 79.3 | |
| Cardiovascular disease (e.g. hypertension, heart disease) | 253 | 80.3 | |
| Urinary system diseases (e.g. nephritis, kidney stones) | 35 | 11.1 | |
| Digestive System diseases (e.g. gastritis, hepatitis) | 116 | 36.9 | |
| Infectious diseases | 221 | 70.4 | |
| Others | 5 | 1.6 | |
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| Air-borne diseases | 219 | 69.7 | |
| Water-borne diseases | 213 | 67.8 | |
| Directly contact transmitted diseases | 64 | 20.4 | |
| Food-borne diseases | 156 | 49.7 | |
| Vector-borne diseases | 244 | 77.7 | |
| Soil-borne diseases | 93 | 29.6 | |
| Rodent-borne diseases | 42 | 13.4 | |
| Vertical transmitted diseases | 25 | 8.0 | |
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| Extremely likely | 72 | 22.9 | |
| Very likely | 145 | 46.2 | |
| Somewhat likely | 87 | 27.7 | |
| Less likely | 10 | 3.2 | |
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| Extremely likely | 91 | 29.0 | |
| Very likely | 150 | 47.8 | |
| Somewhat likely | 63 | 20.1 | |
| Less likely | 10 | 3.2 | |
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| Considered and conducted related researches | 27 | 8.6 | |
| Just think about | 238 | 75.8 | |
| Not at all considered | 49 | 15.6 | |
*Percentage total may add up to more than 100% as multiple responses were permissible.
CDC staff's perceptions towards infectious disease surveillance and scientific research of response measures (N = 314).
| Response measures to climate change | EI | VI | JS | UI | |
| n (%) | n (%) | n (%) | n (%) | ||
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| Improve the quality of disease surveillance data | 175 (55.7) | 113 (36.0) | 21 (6.7) | 5 (1.6) | |
| Strengthen the surveillance of infectious diseases, especially vector-borne diseases, waterborne and food-borne disease | 181 (57.6) | 123 (39.2) | 8 (2.6) | 2 (0.6) | |
| Vector surveillance (such as mosquitoes and other insects) | 115 (36.6) | 156 (49.7) | 41 (13.1) | 2 (0.6) | |
| Meteorological variable observation | 81 (25.8) | 133 (42.4) | 86 (27.3) | 14 (4.5) | |
| Vector breeding site surveillance | 96 (30.6) | 157 (50.0) | 51 (16.2) | 10 (3.2) | |
| Vulnerable groups surveillance and protection | 80 (25.5) | 154 (49.0) | 63 (20.1) | 17 (5.4) | |
| Clinical monitoring of patients | 62 (19.7) | 138 (43.9) | 97 (31.0) | 17 (5.4) | |
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| Enhancing surveillance and projection capacities | 77 (24.5) | 156 (49.7) | 70 (22.3) | 11 (3.5) | |
| Assessing the risk of spreading infectious diseases due to climate change | 92 (29.3) | 160 (51.0) | 52 (16.5) | 10 (3.2) | |
| Identifying high risk climatic zones | 77 (24.5) | 138 (43.9) | 83 (26.5) | 16 (5.1) | |
| Improving emergency response mechanisms for disease outbreaks | 105 (33.4) | 145 (46.2) | 47 (15.0) | 17 (5.4) | |
| Increasing investment in scientific research associated with addressing climate change | 133 (42.4) | 122 (38.9) | 48 (15.2) | 11 (3.5) | |
Note: EI = Extremely important; VI = Very important; JS = Just so so; UI = Unimportant.
CDC staff's perceptions towards capacity building, legislation and health intervention measures of infectious diseases (N = 314).
| Response measures to climate change | EI | VI | JS | UI | |
| n (%) | n (%) | n (%) | n (%) | ||
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| Infrastructure development/refinement (e.g. improve disease surveillance platform, online disease notification system) | 142 (45.2) | 129 (41.1) | 39 (12.4) | 4 (1.3) | |
| Staff in-house training | 114 (36.3) | 154 (49.0) | 40 (12.7) | 6 (1.9) | |
| Cross department information sharing | 105 (33.4) | 148 (47.1) | 53 (16.9) | 8 (2.5) | |
| Community Health education | 103 (32.8) | 163 (51.9) | 43 (13.7) | 5 (1.6) | |
|
| 102 (32.5) | 125 (39.8) | 73 (23.2) | 14 (4.5) | |
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| 116 (36.9) | 146 (46.5) | 41 (13.1) | 11 (3.5) | |
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| Improve living conditions | 104 (33.1) | 147 (46.8) | 53 (16.9) | 10 (3.2) | |
| Individual protection | 110 (35.0) | 156 (49.7) | 38 (12.1) | 10 (3.2) | |
| Food safety | 118 (37.6) | 144 (45.9) | 43 (13.7) | 9 (2.9) | |
| Control the environment of vector breeding sites | 124 (39.5) | 154 (49.0) | 29 (9.2) | 7 (2.2) | |
| Improve drinking water and sanitation | 122 (38.9) | 145 (46.2) | 36 (11.5) | 11 (3.5) | |
| Insecticide use, mosquito control, deratization, etc. | 141 (44.9) | 126 (40.1) | 30 (9.6) | 17 (5.4) | |
Note: EI = Extremely important; VI = Very important; JS = Just so so; UI = Unimportant.
CDC staff's perceptions towards future strategies and measures (N = 314).
| How important are these strategies and measures? | EI | VI | JS | UI | NA |
| n (%) | n (%) | n (%) | N (%) | n (%) | |
| Prevention first | 214 (68.2) | 45 (14.3) | 14 (4.5) | 7 (2.2) | 34 (10.8) |
| Strengthen international cooperation | 101 (32.2) | 93 (29.6) | 47 (15.0) | 18 (5.7) | 55 (17.5) |
| Establish a global infectious disease monitoring and response system for information sharing | 163 (51.9) | 94 (29.9) | 24 (7.6) | 5 (1.6) | 28 (8.9) |
| Drinking water safety | 92 (29.3) | 120 (38.2) | 37 (11.8) | 12 (3.8) | 53 (16.9) |
| Timely and effectively coordinate health action in an emergency event | 71 (22.6) | 135 (43.0) | 37 (11.8) | 10 (3.2) | 61 (19.4) |
| Provide high space-time resolution of data which could be incorporated into GIS in future | 37 (11.8) | 103 (32.8) | 88 (28.0) | 15 (4.8) | 71 (22.6) |
| Promote adaptation actions through in-house training and legislation | 28 (8.9) | 79 (25.2) | 76 (24.2) | 44 (14.0) | 87 (27.7) |
| Behavior change and medical intervention | 24 (7.6) | 48 (15.3) | 85 (27.1) | 65 (20.7) | 92 (29.3) |
Note: EI = Extremely important; VI = Very important; JS = Just so so; UI = Unimportant; NA = No answer was given.
Multivariate ordinal logistic regression between demographic variables and measures related to climate change.
| Dependent variables | Independent variables |
| 95% |
|
| Strengthen the surveillance of infectious diseases | Educational level (At or above undergraduate/Below undergraduate) | 1.900 | 1.022–3.532 | 0.042 |
| Emergency response and management (Yes/No) | 4.415 | 1.682–11.577 | 0.003 | |
| Vector surveillance | Levels of CDC (Provincial/District and county) | 2.710 | 1.119–6.303 | 0.027 |
| Emergency response and management (Yes/No) | 3.916 | 1.751–8.767 | 0.001 | |
| Decision-making coordination among government departments | Educational level (At or above undergraduate/Below undergraduate) | 1.802 | 1.021–3.180 | 0.042 |
| Unit Manager (Yes/No) | 1.826 | 1.082–3.080 | 0.024 |