J Korean Med Assoc Search

CLOSE


J Korean Med Assoc > Volume 57(3); 2014 > Article
Kim, Kim, Lee, Kim, and Choi: Public health concerns and risk perceptions in Korea: Focusing on the residents of the metropolitan cities

Abstract

This study aimed to measure the variation in the levels of risk perception associated with various health risk factors. We analyzed the variables of psychological paradigms that may affect such risk perception levels. According to the perception survey results, the perception of the risk of medical malpractice appeared to be at the highest level compared to other risk factors. According to the analysis of differences in psychological paradigms of health risk factors between genders, the known extent of hazard that medical malpractice, medicines side effects, vaccination accidents, acquired immune deficiency syndrome (AIDS), and food poisoning was much high in female than in male. According to the evaluation of the severity of the risk to future generations, it appeared that women believed that vaccination accidents, AIDS, chronic diseases such as diabetes and hypertension, smoking, and drinking would have a greater effect on the risk to future generations than did men. The significance of this study is that the psychological paradigm affecting the perception level of health risk factors and the risk perceptions themselves have been analyzed by a survey of adults from the general population of Korea.

Figure 1.
Health risk and psychological paradigm risk cognitive map. a)Eigen value, 1.032; variance (%), 68.96. b)Eigen value, 2.416; variance (%), 48.32.
jkma-57-259f1-l.jpg
Table 1.
Example of survey question for ‘How much do you know about the hazards of health risk factors?
Factors Do not know <———————-> Very know
Medical malpractice
Medicine side effect
Vaccine accident
Acquired immune deficiency syndrome
Food poisoning
Swine flu
Chronic disease (e.g., diabetes, hypertension, etc.)
Smoking
Drinking
1
1
1
1
1
1
1
1
1
2
2
2
2
2
2
2
2
2
3
3
3
3
3
3
3
3
3
4
4
4
4
4
4
4
4
4
5
5
5
5
5
5
5
5
5
6
6
6
6
6
6
6
6
6
7
7
7
7
7
7
7
7
7
Table 2.
Socio-demographic characteristics of subjects by gender
  Variable Total (n=1,001) Gender
Male (n=499) Female (n=502)
Age (yr) 20-29 212 (21.2) 108 (21.6) 104 (20.7)
  30-39 234 (23.4) 117 (23.5) 117 (23.3)
  40-49 240 (24.0) 117 (23.5) 123 (24.5)
  50-59 199 (19.9) 97 (19.4) 102 (20.3)
  ≥60 116 (11.6) 60 (12.0) 56 (11.2)
Education level Less than middle school 18 (1.8) 3 (0.7) 15 (3.0)
High school graduate 230 (23.0) 76 (15.2) 154 (30.7)
Currently enrolled in university 112 (11.2) 63 (12.6) 49 (9.8)
College education 568 (56.7) 305 (61.1) 263 (52.3)
Higher than graduate school graduation 73 (7.3) 52 (10.4) 21 (4.2)
Region Seoul 453 (45.2) 234 (46.9) 219 (43.7)
Busan 154 (15.4) 78 (15.7) 76 (15.1)
Incheon 112 (11.2) 50 (10.0) 62 (12.3)
Daegu 108 (10.8) 55 (11.0) 53 (10.5)
Gwangju 66 (6.6) 31 (6.2) 35 (7.0)
Daejeon 61 (6.1) 27 (5.4) 34 (6.8)
Ulsan 47 (4.7) 24 (4.8) 23 (4.6)
Income (1,000 won) <3,000 309 (30.9) 137 (27.5) 172 (34.3)
<6,000 517 (51.6) 261 (52.3) 256 (51.0)
≥6,000 175 (17.5) 101 (20.2) 74 (14.7)

Values are presented as n (%).

Table 3.
Gender difference of risk perception score and rank (n=1,001)
Variablea) Total (n=1,001) Male (n=499) Female (n=502) t-test
Mean±SD Order Mean±SD Order Mean±SD Order t P-valueb)
Medical malpractice 5.65±1.33 1 5.57±1.31 1 5.73±1.35 1 1.880 0.060
Medicine side effect 5.23±1.29 4 5.12±1.28 4 5.34±1.29 3 2.632 0.009
Vaccine accident 5.12±1.40 7 5.05±1.38 6 5.20±1.43 5 1.751 0.080
Acquired immune deficiency syndrome 5.23±1.29 4 5.01±1.88 7 5.06±1.90 7 0.467 0.640
Food poisoning 5.20±1.23 5 5.18±1.24 3 5.23±1.23 4 0.573 0.182
Swine flu 5.57±1.28 2 5.09±1.13 5 5.19±1.12 6 0.966 0.182
Chronic disease (e.g., diabetes, hypertension, etc.) 5.47±1.36 3 5.48±1.29 2 5.46±1.43 2 -0.29 0.773
Smoking 5.16±1.42 6 5.15±1.35 4 5.19±1.49 6 0.434 0.664
Drinking 4.21±1.41 8 4.37±.130 8 4.05±1.49 8 -3.607 0.000

a) Dependant variable: range 1 to 7, 1=they are not at health risk, to 7=they are very much at health risk.

b) P-value is calculated by independent two-sample t-test (significant at 0.05).

Table 4.
Analysis of risk perception and psychometric paradigms for each of gender
  Variable a) Total (n=1,001)
Male (n=499)
Female (n=502)
t-test
Mean±SD Order Mean±SD Order Mean±SD Order t P-valueb)
Personal knowledge Medical malpractice 5.01±1.28 6 4.95±1.34 6 5.07±1.22 6 -1.481 0.139
Medicine side effect 4.68±1.35 8 4.61±1.40 8 4.75±1.29 7 0.026 0.106
  Vaccine accident 4.41±1.39 9 4.29±1.43 9 4.53±1.34 8 0.267 0.006
  Acquired immune deficiency syndrome 4.99±1.41 7 4.82±1.45 7 5.15±1.35 9 -3.749 0.000
  Food poisoning 5.48±1.14 2 5.52±1.15 2 5.45±1.13 3 0.983 0.326
  Swine flu 5.14±1.13 5 5.19±1.12 5 5.09±1.13 5 1.337 0.182
  Chronic disease (e.g., diabetes, hypertension, etc.) 5.36±1.20 4 5.30±1.24 4 5.41±1.14 4 -1.431 0.153
  Smoking 5.60±1.87 1 5.53±1.26 1 5.67±1.10 1 -1.943 0.052
  Drinking 5.46±1.20 3 5.38±1.23 3 5.54±1.16 2 0.298 0.034
Known extent of hazard Medical malpractice 4.64±1.41 7 4.55±1.42 7 4.73±1.40 7 -1.990 0.047
Medicine side effect 4.56±1.40 8 4.47±1.43 8 4.66±1.40 8 -2.187 0.029
  Vaccine accident 4.45±1.44 9 4.33±1.47 9 4.57±1.38 9 -2.679 0.007
  Acquired immune deficiency syndrome 5.08±1.44 5 4.93±1.49 6 5.24±1.37 6 -3.470 0.001
  Food poisoning 5.66±1.17 1 5.58±1.19 1 5.75±1.15 1 -2.240 0.025
  Swine flu 5.07±1.31 6 5.07±1.33 5 5.08±1.29 5 -0.054 0.957
  Chronic disease (e.g., diabetes, hypertension, etc.) 5.57±1.18 3 5.53±1.19 4 5.60±1.16 3 -1.011 0.321
  Smoking 5.63±1.22 2 5.58±1.23 1 5.68±1.21 2 -1.242 0.215
  Drinking 5.51±1.23 4 5.46±1.23 3 5.56±1.22 4 -1.275 0.203
Controllability Medical malpractice 3.39±1.77 9 3.28±1.72 9 3.49±1.80 9 -0.935 0.065
  Medicine side effect 3.70±1.59 7 3.63±1.61 7 3.77±1.58 7 -1.410 0.159
  Vaccine accident 3.45±1.63 8 3.31±1.59 8 3.58±1.65 8 -2.616 0.009
  Acquired immune deficiency syndrome 4.40±1.70 5 4.20±1.70 6 4.60±1.69 5 -3.699 0.000
  Food poisoning 5.70±1.18 1 5.68±1.72 1 5.71±1.19 2 -0.431 0.667
  Swine flu 5.55±1.18 2 5.52±1.20 2 5.58±1.16 1 -0.739 0.460
  Chronic disease (e.g., diabetes, hypertension, etc.) 4.34±1.59 6 4.29±1.60 5 4.39±1.58 6 -0.935 0.350
  Smoking 4.64±1.78 4 4.58±1.85 4 4.70±1.71 4 -1.080 0.280
  Drinking 4.91±1.76 3 4.95±1.81 3 4.87±1.71 3 0.704 0.482
Seriousness of the risk to future generations Medical malpractice 4.76±1.41 8 4.81±1.43 8 4.70±1.39 8 1.158 0.247
Medicine side effect 5.19±1.28 5 5.25±1.29 6 5.12±1.27 3 1.567 0.117
Vaccine accident 4.88±1.35 7 4.97±1.37 7 4.78±1.33 6 2.233 0.026
  Acquired immune deficiency syndrome 5.44±1.34 3 5.52±1.34 5 4.78±1.33 6 1.927 0.054
  Food poisoning 4.34±1.60 9 4.39±1.61 9 4.28±1.59 9 1.105 0.269
  Swine flu 5.15±1.39 6 5.26±1.36 4 5.08±1.41 4 1.611 0.108
  Chronic disease (e.g., diabetes, hypertension, etc.) 5.41±1.27 2 5.53±1.23 2 5.29±1.30 2 2.907 0.004
  Smoking 5.56±1.24 1 5.69±1.16 1 5.43±1.30 1 3.346 0.001
  Drinking 5.20±1.34 4 5.31±1.30 3 5.08±1.37 4 2.826 0.005
Outrage Medical malpractice 5.79±1.23 1 5.85±1.24 1 5.72±1.23 1 1.789 0.074
  Medicine side effect 5.56±1.18 3 5.64±1.19 3 5.48±1.18 3 2.041 0.041
  Vaccine accident 5.60±1.24 2 5.71±1.22 2 5.49±1.39 2 2.816 0.005
  Acquired immune deficiency syndrome 5.36±1.44 4 5.40±1.47 5 5.33±1.41 4 0.698 0.485
  Food poisoning 4.49±1.37 9 4.47±1.37 9 4.50±1.37 8 -0.311 0.756
  Swine flu 5.28±1.26 5 5.54±1.21 4 5.13±1.29 5 3.714 0.000
  Chronic disease (e.g., diabetes, hypertension, etc.) 4.74±1.40 7 4.80±1.41 7 4.68±1.39 9 1.353 0.176
  Smoking 5.17±1.41 6 5.32±1.45 6 5.02±1.35 6 3.416 0.001
  Drinking 4.70±1.43 8 4.75±1.50 8 4.65±1.36 7 1.079 0.281

a) Dependant variable: range 1 to 7, 1=negative score, to 7=positive score.

b) P-value is calculated by independent two-sample t-test (significant at 0.05).

Table 5.
Analysis of risk perception for ‘ personal responsibility’ variables
Variable a) Total (n=1,001)
Male (n=499)
Female (n=502)
t-test
Mean±SD Order Mean±SD Order Mean±SD Order t P-value b)
Medical malpractice 2.40±1.54 9 2.55±1.56 9 2.26±1.51 9 -3.009 0.003
Medicine side effect 2.96±1.54 7 3.05±1.56 7 2.86±1.52 7 -2.027 0.043
Vaccine accident 2.55±1.53 8 2.67±1.52 8 2.43±1.53 8 -2.455 0.014
Acquired immune deficiency syndrome 4.01±1.76 5 4.33±1.72 5 3.70±1.74 6 -5.760 0.000
Food poisoning 4.71±1.50 4 4.64±1.52 4 4.79±1.47 2 1.562 0.119
Swine flu 3.83±1.50 6 3.80±1.47 6 3.87±1.53 5 0.769 0.442
Chronic disease (e.g., dia betes, hypertension, etc.) 4.73±1.50 3 4.78±1.50 3 4.68±1.50 3 -1.121 0.262
Smoking 4.84±1.76 2 5.08±1.59 2 4.61±1.88 4 -4.315 0.000
Drinking 5.96±1.66 1 5.22±1.51 1 4.91±1.78 1 -2.973 0.003

a) Dependant variable: range 1 to 7, 1=the government are responsible for health risk, to 7=the government are not responsible for health risk.

b) P-value is calculated by independent two-sample t-test(significant at 0.05).

References

1. Crouch EA, Zeise RW. The risks of drinking water. Water Resour Res 1983;19:1359-1375.
crossref pdf
2. Kim KH. Consumer's perceptions attributed to food-related risks and risk communication [dissertation]. Seoul: Korea University; 2012.

3. You MS. The study of health-risk perception: implications for health services research. Korean J Health Policy Adm 2009;19:45-70.
crossref
4. Park DG, Jeong UH. History, definitions and tasks of health communication. Health Commun Res 2009;1:33-48.

5. Slovic P. Perception of risk. Science 1987;236:280-285.
crossref pmid
6. Starr G, Langley A, Taylor A. Environmental health risk perception in Australia [Internet]. [place unknown]: Centre for Population Studies in Epidemiology South Australian Department of Human Services; 2000;[cited 2014 Jan 3]. Available from:. http://www.health.gov.au/internet/main/publishing.nsf/Content/health-pubhlth-publicat-document-metadata-envrisk.htm.

7. Kim SJ, Cha H. The effect of public segmentation and message framing on the health risk communication: applying anger activism model. Korean J Journal Commun Stud 2009;53:231-253.

8. Kim H. Development of health communication strategies for health behavior change: application of social ecological models to smoking cessation intervention. Korean J Health Educ Promot 2010;27:177-188.

9. Kim JH, Cho MJ. Gender-specific factors predicting substance abuse: in search of health communication strategies for high risk group. J Korean Med Assoc 2012;55:84-96.
crossref
10. Yoo SJ, Jeong HJ, Park HS. The analysis on factors affecting the intention for H1N1 virus vaccination and the impact of negative news reports: the comparison between HBM and TPB. Korean J Advert Public Relat 2010;12:283-319.

11. Hahm MI, Kwon HJ, Lee HY, Park HG, Lee SG. Differences of experts and non-experts in perceiving environmental and technological risks. J Environ Health Sci 2009;35:269-277.
crossref
12. Slovic P. The perception of risk. Sterling: Earthscan Publications; 2000.

13. Cha YJ. Risk perception and policy implications for risk analysis: with focus on the lay people in the capital region. Korean Policy Stud Rev 2007;16:97-117.

14. Jung JB, Chae JH. The politicization of risk and an effective response strategy purpose and methodology. Seoul: Korea Institute of Public Administration; 2010.

15. Sandman PM. Responding to community outrage: strategies for effective risk communication [Internet]. New York: Risk Communication Website; 2012. [cited 2013 Oct 13]. Available from:. http://www.psandman.com.

16. Lee KH. Study on the emprical analysis and the implications for the effective food risk communication. J Consum Policy Stud 2008;104-133.

17. Maharaj P. Reasons for condom use among young people in KwaZulu-Natal: prevention of HIV, pregnancy or both? Int Fam Plan Perspect 2006;32:28-34.
crossref pmid
18. Maswanya ES, Moji K, Horiguchi I, Nagata K, Aoyagi K, Honda S, Takemoto T. Knowledge, risk perception of AIDS and reported sexual behaviour among students in secondary schools and colleges in Tanzania. Health Educ Res 1999;14:185-196.
crossref pmid
19. Shobo Y. Youth's perceptions of HIV infection risk: a sex-specific test of two risk models. African J AIDS Res 2007;6:1-8.
crossref
20. Kim MJ, Lee SY, Lee KS, Kim A, Son D, Chung MH, Park SG, Park JH, Lee BI, Lee JS. Influenza vaccine coverage rate and related factors on pregnant women. Infect Chemother 2009;41:349-354.
crossref
21. Han JH. Effects of risk communication in vaccination [dissertation]. Seoul: Yonsei University; 2003.

22. Boholm A. Comparative studies of risk perception: a review of twenty years of research. J Risk Res 1998;1:135-163.
crossref
23. Byrnes JP, Miller DC, Schafer WD. Gender differences in risk taking: a metaanalysis. Psychol Bull 1999;125:367-383.
crossref
24. Wester-Herber M, Warg LE. Gender and regional differences in risk perception: results from implementing the Seveso II Directive in Sweden. J Risk Res 2002;5:69-81.
crossref
25. Crittenden KS. Sociological aspects of attribution. Annu Rev Sociol 1983;9:425-446.
crossref
26. Lee YJ. Attribution and behavioral responses in failed medical service encounters [dissertation]. Seoul: Kyunghee University; 2010.

27. Lee JE, Choi IS. The change in trust toward social commerce companies after failure of social commerce services: focusing on severity and main source of service failure, and brand equity of social commerce companies. Korean J Consum Advert Psychol 2011;12:799-824.
crossref
28. Kim KH, Song DJ, Choi JW. A Study on risk communication and perception of electromagnetic waves from cellular phones: focus on risk perception of women. J Korea Inst Electron Commun Sci 2013;8:1065-1074.
crossref
29. Choi CW, Jeong JY, Hwang MS, Jung KK, Lee HM, Lee KH. Risk communication study for nanotechnology using risk cognitive map. Environ Health Toxicol 2010;25:187-195.

TOOLS
Share :
Facebook Twitter Linked In Google+ Line it
METRICS Graph View
  • 0 Crossref
  • 2 Scopus
  • 1,846 View
  • 20 Download
Related articles in
J Korean Med Assoc


ABOUT
ARTICLE CATEGORY

Browse all articles >

ARCHIVES
FOR CONTRIBUTORS
Editorial Office
37 Ichon-ro 46-gil, Yongsan-gu, Seoul
Tel: +82-2-6350-6562    Fax: +82-2-792-5208    E-mail: jkmamaster@gmail.com                

Copyright © 2024 by Korean Medical Association.

Developed in M2PI

Close layer
prev next