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Study on the disease burden and prediction of substance use disorder in China based on age-period-cohort model

Published on Aug. 01, 2024Total Views: 1074 times Total Downloads: 227 times Download Mobile

Author: BI Hui MA Danhua XU Guili HUA Yunpeng XING Liang

Affiliation: Department of Pharmacy, The Air Force Hospital of Eastern Theater of PLA, Nanjing 210002, China

Keywords: Drug abuse Substance use disorder Disease burden Trend analysis Age-period-cohort model

DOI: 10.12173/j.issn.1005-0698.202405024

Reference: BI Hui, MA Danhua, XU Guili, HUA Yunpeng, XING Liang.Study on the disease burden and prediction of substance use disorder in China based on age-period-cohort model[J].Yaowu Liuxingbingxue Zazhi,2024, 33(7):760-769.DOI: 10.12173/j.issn.1005-0698.202405024.[Article in Chinese]

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Abstract

Objective  To analyze the incidence and disease burden of substance use disorder (SUD) in China from 1990 to 2019, to evaluate the impact of different ages, periods and birth cohorts on the disease burden of SUD, and to predict disease burden of SUD from 2020 to 2034, so as to provide strategies for the prevention of SUD.

Methods  Based on the Global Burden of Disease Study 2019 (GBD 2019) database, the disease burden was described by incidence, years of life lost (YLLs), years lived with disability (YLDs) and disability-adjusted life years (DALYs). The Joinpoint regression model was used to analyze the trend of standardized incidence and standardized DALYs rate of SUD. Based on the age-period-cohort model, the age, period and cohort effects of SUD were discussed. The grey prediction model GM (1,1) was used to fit the trend of the incidence and standardized incidence of SUD and the trend of disease burden, and to predict the incidence and disease burden of SUD in 2020-2034.

Results  From 1990 to 2019, the standardized incidence of SUD of amphetamines [average annual percentage change (AAPC)=-0.9%] and cocaine (AAPC=- 0.5%) in China showed a downward trend (P<0.001), and the standardized incidence of SUD of cannabis (AAPC=0.9%) showed an increasing trend year by year (P<0.001). The trend of standardized incidence of opioid abuse disorders was not obvious (P>0.05). The DALYs rate caused by the 4 SUD showed a decreasing trend year by year (AAPCamphetamines=-2.2%, AAPCcocaine=-1.5%, AAPCcannabis=-1.0%, AAPCopioids=-1.0%, P<0.001). The results of age-period-cohort effect showed that the peak incidence of amphetamine, cocaine, cannabis and opioid use disorders was in the 25-30 age group. The DALYs rate caused by cannabis SUD increased with age, while the DALYs rates of amphetamines, cocaine and opioids SUD reached the peak in the 25-29, 30-34 and 35-39 age groups, respectively. The results of period effect showed that the risk of SUD in propylamines, cocaine and cannabis decreased first and then increased, while the risk of SUD in opioids increased and then decreased and increased again. The results of birth cohort effect showed that the risk of SUD of amphetamines, cocaine and opioids showed a decreasing trend as a whole except for a small fluctuation in individual birth cohorts. The risk of DALYs rate caused by SUD of amphetamines, cocaine and opioids showed a decreasing trend as a whole, while the risk of DALYs rate caused by SUD of cannabis showed an increasing trend year by year. The prediction results showed that the incidence of SUD of amphetamines, cocaine and opioids showed a downward trend from 2020 to 2034, and the incidence of SUD of cannabis showed a fluctuating upward trend. The DALYs attributed to SUD of amphetamines, cocaine, cannabis and opioids showed a decreasing trend year by year.

Conclusion  The disease burden of SUD in China is decreasing year by year in the future. The incidence and disease burden are affected by age effect, period effect and cohort effect to varying degrees. Early prevention and effective intervention are the key measures to control SUD.

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References

1.United Nations Office on Drugs and Crime. United Nations Office on Drugs and Crime (UNODC) world drug report 2023[EB/OL]. (2023-06-26) [2024-01-30]. https://www.unodc.org/unodc/en/about-unodc/annual-report.html.

2.Battle DE. Diagnostic and statistical manual of mental disorders, 5th ed[J]. Codas, 2013, 25(2): 191-192. DOI: 10.1590/s2317-17822013000200017.

3.陈帅锋, 甄橙, 史录文. 中国麻醉药品和精神药品管制品种目录变动历程研究(1949—2019年)[J].中国新药杂志, 2021, 30(11): 989-996. [Chen SF, Zhen C, Shi LW. Study on the changing process of the narcotic drugs and psychotropic substances catalogues of China (1949—2019)[J]. Chinese Journal of New Drugs, 2021, 30(11): 989-996.] DOI: 10.3969/j.issn.1003-3734.2021.11.006.

4.周虎子威, 张云静, 于玥琳, 等. 机器学习方法在预测麻精药品不合理使用风险中的应用现状和思考[J]. 药物流行病学杂志, 2023, 32(4): 446-457. [Zhou HZW, Zhang YJ, Yu YL, et al. Application of machine learning methods in predicting the risk of irrational use of narcotic and psychotropic drugs:current status and considerations[J]. Chinese Journal of Pharmacoepidemiology, 2023, 32(4): 446-457.] DOI: 10.19960/j.issn.1005-0698.202304010.

5.GBD 2019 Risk Factors Collaborators. Global burden of 87 risk factors in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019[J]. Lancet, 2020, 396(10258): 1223-1249. DOI: 10.1016/S0140-6736(20)30752-2.

6.杨晓雨, 陈东宇, 王红心, 等. 1990—2019年中国疾病负担趋势分析[J]. 医学新知, 2022, 32(5): 321-332. [Yang XY, Chen DY, Wang HX, et al. Trend analysis of disease burden in China from 1990 to 2019[J]. Yixue Xinzhi Zazhi, 2022, 32(5): 321-332.] DOI: 10.12173/j.issn.1004-5511.202201016.

7.Kim HJ, Fay MP, Feuer EJ, et al. Permutation tests for joinpoint regression with applications to cancer rates[J]. Stat Med, 2000, 19(3): 335-351. DOI: 10.1002/(sici)1097-0258(20000215)19:3<335::aid-sim336>3.0.co;2-z.

8.秦宇辰, 郭威. 年度变化百分比与年均变化百分比在医学研究变化趋势分析中的应用[J]. 中国卫生统计, 2022(3): 470-473. [Qin YC,Guo W. Application of annual percentage change and annual average percentage change in trend analysis of medical research[J]. Chinese Journal of Health Statistics, 2022(3): 470-473.] DOI: 10.3696/jssn.1002-3674.2022.03.036.

9.Bell A. Age period cohort analysis: a review of what we should and shouldn't do[J]. Ann Hum Biol, 2020, 47(2): 208-217. DOI: 10.1080/03014460.2019.1707872.

10.Rosenberg PS. A new age-period-cohort model for cancer surveillance research[J]. Stat Methods Med Res,2019, 28(10-11): 3363-3391. DOI: 10.1177/ 0962280218801121.

11.邓聚龙, 主编. 灰色预测与灰决策[M]. 武汉: 华中科技大学出版社, 2002: 1-235.

12.黄照, 马文军. 年龄-时期-队列模型[J]. 华南预防医学, 2017, 43(4): 373-376. [Huang Z, Ma WJ. Age-period-cohort model[J]. South China Journal of Preventive Medicine, 2017, 43(4): 373-376.] DOI: 10.13217/j.scjpm.2017.0373.

13.Martins SS, Segura LE, Santaella-Tenorio J, et al. Prescription opioid use disorder and heroin use among 12-34 year-olds in the United States from 2002 to 2014[J]. Addict Behav, 2017, 65: 236-241. DOI: 10.1016/j.addbeh.2016.08.033.

14.Saloner B, Bachhuber M, Barry CL. Physicians as a source of medications for nonmedical use: comparison of opioid analgesic, stimulant, and sedative use in a national sample[J]. Psychiatr Serv, 2017, 68(1): 56-62. DOI: 10.1176/appi.ps.201500245.

15.Stone AL, Becker LG, Huber AM, et al. Review of risk and protective factors of substance use and problem use in emerging adulthood[J]. Addict Behav, 2012, 37(7): 747-775. DOI: 10.1016/j.addbeh.2012.02.014.

16.Sinha R. Chronic stress, drug use, and vulnerability to addiction[J]. Ann N Y Acad Sci, 2008, 1141: 105-130. DOI: 10.1196/annals.1441.030.

17.Ford JA, Rigg KK. Racial/ethnic differences in factors that place adolescents at risk for prescription opioid misuse[J]. Prev Sci, 2015, 16(5): 633-641. DOI: 10.1007/s11121-014-0514-y.

18.Rudolph KD, Lambert SF, Clark AG, et al. Negotiating the transition to middle school: the role of self-regulatory processes[J]. Child Dev, 2001, 72(3): 929-946. DOI: 10.1111/1467-8624.00325.

19.Franklin G, Sabel J, Jones CM, et al. A comprehensive approach to address the prescription opioid epidemic in Washington State: milestones and lessons learned[J]. Am J Public Health, 2015, 105(3): 463-469. DOI: 10.2105/AJPH.2014.302367.

20.Schneider FH. Amphetamine-induced exocytosis of catecholamines from the cow adrenal medulla[J]. J Pharmacol Exp Ther, 1972, 183(1): 80-89. DOI: 10.2105/AJPH.2014.302367.

21.Gu H, Salmeron BJ, Ross TJ, et al. Mesocorticolimbic circuits are impaired in chronic cocaine users as demonstrated by resting-state functional connectivity[J]. Neuroimage, 2010, 53(2): 593-601. DOI: 10.1016/j.neuroimage.2010.06.066.

22.Alzghoul BN, Abualsuod A, Alqam B, et al. Cocaine use and pulmonary hypertension[J]. Am J Cardiol, 2020, 125(2): 282-288. DOI: 10.1016/j.amjcard.2019.10.008.

23.Mahoney JJ, Thompson-Lake DG, Cooper K, et al. A comparison of impulsivity, depressive symptoms, lifetime stress and sensation seeking in healthy controls versus participants with cocaine or methamphetamine use disorders[J]. J Psychopharmacol, 2015, 29(1): 50-56. DOI: 10.1177/0269881114560182.

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