Development and Validation of a Risk Prediction Tool to Identify People at Greater Risk of Having Hepatitis C among Drug Users
Abstract
:1. Introduction
2. Materials and Methods
2.1. Subject Recruitment and Study Design
2.2. Data Collection
2.3. Statistical Analysis
2.4. Ethics
3. Results
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Characteristics | Total (n = 17,871) | Positive HCV Antibody Status, n (%) | Negative HCV Antibody Status, n (%) | Unadjusted OR (95% CI) | p-Value |
---|---|---|---|---|---|
Sex | <0.001 | ||||
Female | 2432 | 511 (21.0) | 1921 (79.0) | 1.21 (1.09–1.34) | |
Male | 15,439 | 2788 (18.1) | 12,651 (81.9) | ||
Household registration | 0.610 | ||||
Zhejiang Province | 11,638 | 2161 (18.6) | 9477 (81.4) | 0.98 (0.91–1.06) | |
Other provinces | 6233 | 1138 (18.3) | 5095 (81.7) | ||
Age (years) | <0.001 | ||||
≤30 | 4333 | 366 (8.4) | 3967 (91.6) | ||
31–40 | 6994 | 1180 (16.9) | 5814 (83.1) | 2.20 (1.94–2.49) | |
≥41 | 6544 | 1753 (26.8) | 4791 (73.2) | 3.97 (3.52–4.47) | |
Marital status | 0.013 | ||||
Married/cohabiting | 11,025 | 2098 (19.0) | 8927 (81.0) | 1.11 (1.02–1.20) | |
Divorced/single | 6846 | 1201 (17.5) | 5645 (82.5) | ||
Education | <0.001 | ||||
Illiteracy | 1343 | 329 (24.5) | 1014 (75.5) | 2.79 (2.10–3.70) | |
Primary school | 4446 | 928 (20.9) | 3518 (79.1) | 2.27 (1.74–2.96) | |
Middle school | 8819 | 1553 (17.6) | 7266 (82.4) | 1.84 (1.41–2.38) | |
High school/technical secondary school | 2630 | 423 (16.1) | 2207 (83.9) | 1.65 (1.25–2.17) | |
College degree or above | 633 | 66 (10.4) | 567 (89.6) | ||
Sexual activity after taking drugs | 0.914 | ||||
Yes | 7122 | 1312 (18.4) | 5810 (81.6) | 1.00 (0.92–1.08) | |
No | 10,749 | 1987 (18.5) | 8764 (81.5) | ||
Heroin use | <0.001 | ||||
Yes | 5359 | 1922 (35.9) | 3437 (64.1) | 4.52 (4.18–4.89) | |
No | 12,512 | 1377 (11.0) | 11,135 (89.0) | ||
Cocaine use | 0.215 | ||||
Yes | 75 | 18 (24.0) | 57 (76.0) | 1.40 (0.82–2.38) | |
No | 17,796 | 3281 (18.4) | 14,515 (81.6) | ||
Opium use | 0.624 | ||||
Yes | 33 | 5 (15.2) | 28 (84.8) | 0.79 (0.30–2.04) | |
No | 17,838 | 3294 (18.5) | 14,544 (81.5) | ||
Cannabis sativa use | 0.157 | ||||
Yes | 150 | 21 (14.0) | 129 (86.0) | 0.72 (0.45–1.14) | |
No | 17,721 | 3278 (18.5) | 14,443 (81.5) | ||
Morphine use | 0.023 | ||||
Yes | 139 | 36 (25.9) | 103 (74.1) | 1.55 (1.06–2.27) | |
No | 17,732 | 3263 (18.4) | 14,469 (81.6) | ||
Methamphetamine use | <0.001 | ||||
Yes | 12,316 | 1514 (12.3) | 10,802 (87.7) | 0.30 (0.27–0.32) | |
No | 5555 | 1785 (32.1) | 3770 (67.9) | ||
Demerol | 0.256 | ||||
Yes | 172 | 26 (15.1) | 146 (84.9) | 0.79 (0.52–1.19) | |
No | 17,699 | 3273 (18.5) | 14,426 (81.5) | ||
Ketamine use | 0.058 | ||||
Yes | 240 | 33 (13.8) | 207 (86.2) | 0.70 (0.49–1.01) | |
No | 17,631 | 3266 (18.5) | 14,365 (81.5) | ||
Ecstasy use | 0.112 | ||||
Yes | 98 | 12 (12.2) | 86 (87.8) | 0.62 (0.34–1.13) | |
No | 17,773 | 3287 (18.5) | 14,486 (81.5) | ||
Magu use | <0.001 | ||||
Yes | 556 | 59 (10.6) | 497 (89.4) | 0.52 (0.39–0.68) | |
No | 17,315 | 3240 (18.7) | 14,075 (81.3) |
Variables in the Final Model | B | p-Value | Adjusted OR | 95% CI | Points |
---|---|---|---|---|---|
Sex (compared to males) | 0.39 | <0.001 | 1.48 | 1.32–1.65 | 2 |
Marital status (compared to divorced/single) | |||||
Married | 0.34 | <0.001 | 1.41 | 1.29–1.54 | 2 |
Education (compared to college degree or above) | |||||
Illiteracy | 0.41 | 0.007 | 1.51 | 1.12–2.04 | 3 |
Primary school | 0.46 | 0.001 | 1.59 | 1.20–2.10 | 3 |
Middle school | 0.45 | 0.001 | 1.56 | 1.19–2.05 | 3 |
High school or technical secondary school | 0.34 | 0.022 | 1.4 | 1.05–1.87 | 2 |
Heroin use | 1.5 | <0.001 | 4.5 | 3.88–5.22 | 9 |
Morphine use | 0.54 | 0.011 | 1.71 | 1.14–2.59 | 3 |
Methamphetamine use | 0.16 | 0.039 | 1.17 | 1.01–1.36 | 1 |
Age group (compared to ≤30 years) | |||||
31–40 | 0.82 | <0.001 | 2.28 | 2.00–2.60 | 5 |
>40 | 1.28 | <0.001 | 3.6 | 3.14–4.12 | 8 |
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Huang, G.; Cheng, W.; Xu, Y.; Yang, J.; Jiang, J.; Pan, X.; Zhou, X.; Jiang, J.; Chai, C. Development and Validation of a Risk Prediction Tool to Identify People at Greater Risk of Having Hepatitis C among Drug Users. Int. J. Environ. Res. Public Health 2022, 19, 15677. https://doi.org/10.3390/ijerph192315677
Huang G, Cheng W, Xu Y, Yang J, Jiang J, Pan X, Zhou X, Jiang J, Chai C. Development and Validation of a Risk Prediction Tool to Identify People at Greater Risk of Having Hepatitis C among Drug Users. International Journal of Environmental Research and Public Health. 2022; 19(23):15677. https://doi.org/10.3390/ijerph192315677
Chicago/Turabian StyleHuang, Gang, Wei Cheng, Yun Xu, Jiezhe Yang, Jun Jiang, Xiaohong Pan, Xin Zhou, Jianmin Jiang, and Chengliang Chai. 2022. "Development and Validation of a Risk Prediction Tool to Identify People at Greater Risk of Having Hepatitis C among Drug Users" International Journal of Environmental Research and Public Health 19, no. 23: 15677. https://doi.org/10.3390/ijerph192315677