Comparison of Three Prediction Models for Predicting Chronic Obstructive Pulmonary Disease in China

References

• Vogelmeier CF, Criner GJ, Martinez FJ, et al. Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Lung Disease 2017 Report. Am J Respir Crit Care Med. 2017;195(5):557–582. doi:10.1164/rccm.201701-0218PP
   PubMed Web of Science ®Google Scholar
• Celli B, Fabbri L, Criner G, et al. Definition and Nomenclature of Chronic Obstructive Pulmonary Disease: time for Its Revision. Am J Respir Crit Care Med. 2022;206(11):1317–1325. doi:10.1164/rccm.202204-0671PP
   PubMed Web of Science ®Google Scholar
• Global Health Data Exchange (GHDx) [homepage on the Internet]. GBD Results Tool; 2023. Available from: https://vizhub.healthdata.org/gbd-results/. Accessed February 3, 2023.
   Google Scholar
• GBD 2017 Causes of Death Collaborators. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2018;392(10159):1736–1788. doi:10.1016/S0140-6736(18)32203-7
   PubMed Web of Science ®Google Scholar
• Yin P, Wu J, Wang L, et al. The Burden of COPD in China and Its Provinces: findings From the Global Burden of Disease Study 2019. Front Public Health. 2022;10:859499. doi:10.3389/fpubh.2022.859499
   PubMedGoogle Scholar
• Clements MS, Armstrong BK, Moolgavkar SH. Lung cancer rate predictions using generalized additive models. Biostatistics. 2005;6(4):576–589. doi:10.1093/biostatistics/kxi028
   PubMed Web of Science ®Google Scholar
• Zhu D, Zhou D, Li N, Han B. Predicting Diabetes and Estimating Its Economic Burden in China Using Autoregressive Integrated Moving Average Model. Int J Public Health. 2022;66:1604449. doi:10.3389/ijph.2021.1604449
   PubMed Web of Science ®Google Scholar
• Jian Y, Zhu D, Zhou D, et al. ARIMA model for predicting chronic kidney disease and estimating its economic burden in China. BMC Public Health. 2022;22(1):2456. doi:10.1186/s12889-022-14959-z
   PubMed Web of Science ®Google Scholar
• Tomar A, Gupta N. Prediction for the spread of COVID-19 in India and effectiveness of preventive measures. Sci Total Environ. 2020;728:138762. doi:10.1016/j.scitotenv.2020.138762
   PubMed Web of Science ®Google Scholar
• Hastie T, Tibshirani R. Generalized Additive Models. Boca Raton: CRC Press; 1990.
   Google Scholar
• Xiao D, Wu K, Tan X, et al. Modeling and predicting hemorrhagic fever with renal syndrome trends based on meteorological factors in Hu County, China. PLoS One. 2015;10(4):56.
   Web of Science ®Google Scholar
• Hastie T, Tibshirani R. Generalized additive models: some applications. J Am Stat Assoc. 1987;82(398):371–386. doi:10.1080/01621459.1987.10478440
   Web of Science ®Google Scholar
• Newbold P. ARIMA model building and the time series analysis approach to forecasting. J Forecasting. 1983;2(1):23–35. doi:10.1002/for.3980020104
   Web of Science ®Google Scholar
• Sit V, Poulin-Costello M. Catalogue of Curves for Curve Fitting. Columbia: Forest Science Research Branch, Ministry of Forests; 1994.
   Google Scholar
• Li M, Li LD. A novel method of curve fitting based on optimized extreme learning machine. Appl Artif Intell. 2020;34(12):849–865. doi:10.1080/08839514.2020.1787677
   Web of Science ®Google Scholar
• Wang J, Wang C, Zhang W. Data analysis and forecasting of tuberculosis prevalence rates for smart healthcare based on a novel combination model. Appl Sci. 2018;8(9):1693. doi:10.3390/app8091693
   Google Scholar
• Ravindra K, Rattan P, Mor S, Aggarwal AN. Generalized additive models: building evidence of air pollution, climate change and human health. Environ Int. 2019;132:104987. doi:10.1016/j.envint.2019.104987
   PubMed Web of Science ®Google Scholar
• Fu T, Chen T, Dong ZB, et al. Development and comparison of forecast models of hand-foot-mouth disease with meteorological factors. Sci Rep. 2019;9(1):15691. doi:10.1038/s41598-019-52044-5
   PubMedGoogle Scholar
• Zhou D, Zhu D, Li N, Han B. Exploration of Three Incidence Trend Prediction Models Based on the Number of Diagnosed Pneumoconiosis Cases in China From 2000 to 2019. J Occup Environ Med. 2021;63(7):e440–e444. doi:10.1097/JOM.0000000000002258
   PubMed Web of Science ®Google Scholar
• Zhu Z, Zhu X, Zhan Y, Gu L, Chen L, Li X. Development and comparison of predictive models for sexually transmitted diseases-AIDS, gonorrhea, and syphilis in China, 2011-2021. Front Public Health. 2022;10:966813. doi:10.3389/fpubh.2022.966813
   PubMed Web of Science ®Google Scholar
• Singh RK, Rani M, Bhagavathula AS, et al. Prediction of the COVID-19 Pandemic for the Top 15 Affected Countries: advanced Autoregressive Integrated Moving Average (ARIMA) Model. JMIR Public Health Surveill. 2020;6(2):e19115. doi:10.2196/19115
   PubMedGoogle Scholar
• Alabdulrazzaq H, Alenezi MN, Rawajfih Y, Alghannam BA, Al-Hassan AA, Al-Anzi FS. On the accuracy of ARIMA based prediction of COVID-19 spread. Results Phys. 2021;27:104509. doi:10.1016/j.rinp.2021.104509
   PubMed Web of Science ®Google Scholar
• Xie L. Time series analysis and prediction on cancer incidence rates. J Med Discovery. 2017;2(3):1–10. doi:10.24262/jmd.2.3.17030
   Google Scholar
• Cai Q, Chen Y, Xin S, et al. Temporal trends of bladder cancer incidence and mortality from 1990 to 2016 and projections to 2030. Translational Androl Urol. 2020;9(2):153–165. doi:10.21037/tau.2020.02.24
   PubMed Web of Science ®Google Scholar
• Achterberg MA, Prasse B, Ma L, Trajanovski S, Kitsak M, Van Mieghem P. Comparing the accuracy of several network-based COVID-19 prediction algorithms. Int J Forecast. 2022;38(2):489–504. doi:10.1016/j.ijforecast.2020.10.001
   PubMed Web of Science ®Google Scholar
• Halbert RJ, Natoli JL, Gano A, Badamgarav E, Buist AS, Mannino DM. Global burden of COPD: systematic review and meta-analysis. Eur Respir J. 2006;28(3):523–532. doi:10.1183/09031936.06.00124605
   PubMed Web of Science ®Google Scholar
• Wang C, Xu J, Yang L, et al.; China Pulmonary Health Study Group. Prevalence and risk factors of chronic obstructive pulmonary disease in China (the China Pulmonary Health [CPH] study): a national cross-sectional study. Lancet. 2018;391(10131):1706–1717. doi:10.1016/S0140-6736(18)30841-9
   PubMed Web of Science ®Google Scholar
• National Bureau of Statistics of China [homepage on the Internet]. Age Composition and Dependency Ratio of Population; 2022. Available from: https://data.stats.gov.cn/english/easyquery.htm?cn=C01. Accessed February 15, 2023.
   Google Scholar
• Fang EF, Xie C, Schenkel JA, et al. A research agenda for ageing in China in the 21st century (2nd edition): focusing on basic and translational research, long-term care, policy and social networks. Ageing Res Rev. 2020;64:101174. doi:10.1016/j.arr.2020.101174
   PubMed Web of Science ®Google Scholar
• Bartal M. COPD and tobacco smoke. Monaldi Arch Chest Dis. 2005;63(4):213–225. doi:10.4081/monaldi.2005.623
   PubMedGoogle Scholar
• Adeloye D, Song P, Zhu Y, Campbell H, Sheikh A, Rudan I. NIHR RESPIRE Global Respiratory Health Unit. Global, regional, and national prevalence of, and risk factors for, chronic obstructive pulmonary disease (COPD) in 2019: a systematic review and modelling analysis. Lancet Respir Med. 2022;10(5):447–458. doi:10.1016/S2213-2600(21)00511-7
   PubMed Web of Science ®Google Scholar
• Fang L, Gao P, Bao H, et al. Chronic obstructive pulmonary disease in China: a nationwide prevalence study. Lancet Respir Med. 2018;6(6):421–430. doi:10.1016/S2213-2600(18)30103-6
   PubMed Web of Science ®Google Scholar
• Reitsma MB, Kendrick PJ, Ababneh E; GBD 2019 Tobacco Collaborators. Spatial, temporal, and demographic patterns in prevalence of smoking tobacco use and attributable disease burden in 204 countries and territories, 1990-2019: a systematic analysis from the Global Burden of Disease Study 2019. Lancet. 2021;397(10292):2337–2360. doi:10.1016/S0140-6736(21)01169-7
   PubMed Web of Science ®Google Scholar
• Zhang M, Yang L, Wang L, et al. Trends in smoking prevalence in urban and rural China, 2007 to 2018: findings from 5 consecutive nationally representative cross-sectional surveys. PLoS Med. 2022;19(8):e1004064. doi:10.1371/journal.pmed.1004064
   PubMed Web of Science ®Google Scholar