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Age-Period-Cohort Analysis on the Time Trend of Hepatitis B Incidence in Four Prefectures of Southern Xinjiang, China from 2005 to 2017

Author

Listed:
  • Weidong Ji

    (College of Public Health, Xinjiang Medical University, Urumqi 830011, China
    These authors contributed equally.)

  • Na Xie

    (Xinjiang Center for Disease Control and Prevention, Urumqi 830054, China
    These authors contributed equally.)

  • Daihai He

    (Department of Applied Mathematics, Hong Kong Polytechnic University, Hong Kong, China)

  • Weiming Wang

    (School of Mathematics Science, Huaiyin Normal University, Huaian 223300 China)

  • Hui Li

    (Central Laboratory of Xinjiang Medical University, Urumqi 830011, China)

  • Kai Wang

    (Department of Medical Engineering and Technology, Xinjiang Medical University, Urumqi 830011, China)

Abstract

Objective : The influence of age, period, and cohort on Hepatitis B (HB) incidence in four prefectures of southern Xinjiang, China is still not clear. This paper aims to analyze the long-term trend of the HB incidence in four prefectures of southern Xinjiang, China and to estimate the independent impact of age, period and cohort, as well as to predict the development trend of HB incidence in male and female groups, then to identify the targeted population for HB screening by the model fitting and prediction. Method : The data were from the Case List of HB Cases Reported in the Infectious Disease Reporting Information Management System and the Xinjiang Statistical Yearbook of China. The age-period-cohort (APC) model was used to estimate the impacts of age, period and cohort on HB incidence, which could be used to predict the HB incidence in specific age groups of men and women. Results : Under the influence of age effect, the incidence of HB in males had two peaks (20–35 years old and 60–80 years old), the influence of age effect on the incidence of HB in females was lower than that of males and the obvious peak was between 20–30 years old; the period effect on the HB incidence in males and females fluctuated greatly and the fluctuation degree of influence on males was bigger than that of women. The HB incidence among males and females in the four regions tended to be affected by cohort effect, which reached a peak after 1990 and then declined sharply and gradually became stabilized. By predicting the HB incidence from 2018 to 2022, we found that there were significant differences in HB incidence among people over 35 years old, under 35 years old and the whole population in four prefectures of southern Xinjiang, China. Conclusions : Although the incidence of HB in some regions shows a downward trend, there is still an obvious upward trend of incidences in other places. In our paper, results indicate that the burden of HB incidence may be extended in the future, so we hope this can draw the attention of relative departments. These results reveal the differences of incidence between males and females as well, so respective measures of the two groups’ functions are essential.

Suggested Citation

  • Weidong Ji & Na Xie & Daihai He & Weiming Wang & Hui Li & Kai Wang, 2019. "Age-Period-Cohort Analysis on the Time Trend of Hepatitis B Incidence in Four Prefectures of Southern Xinjiang, China from 2005 to 2017," IJERPH, MDPI, vol. 16(20), pages 1-17, October.
  • Handle: RePEc:gam:jijerp:v:16:y:2019:i:20:p:3886-:d:276165
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    References listed on IDEAS

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    1. D. Kuang & B. Nielsen & J. P. Nielsen, 2008. "Forecasting with the age-period-cohort model and the extended chain-ladder model," Biometrika, Biometrika Trust, vol. 95(4), pages 987-991.
    2. Chi Zhang, 2017. "Population in China," Europe-Asia Studies, Taylor & Francis Journals, vol. 69(8), pages 1333-1334, September.
    3. Bent Nielsen & María Dolores Martínez-Miranda & Jens Perch Nielsen, 2016. "A simple benchmark for mesothelioma projection for Great Britain," Economics Papers 2016-W03, Economics Group, Nuffield College, University of Oxford.
    4. D. Kuang & B. Nielsen & J. P. Nielsen, 2008. "Forecasting with the age-period-cohort model and the extended chain-ladder model," Biometrika, Biometrika Trust, vol. 95(4), pages 987-991.
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