المؤلفون: Wang MW, Sun L, Wen W, Wang J, Wang C, Ni J, Jiang J, Feng ZH, Cheng YR

المصدر: Risk Management and Healthcare Policy, Vol Volume 15, Pp 1751-1759 (2022)

مصطلحات موضوعية: air pollutants, metabolism-associated fatty liver disease, generalized additive model, Public aspects of medicine, RA1-1270

الوصف: Ming-Wei Wang,1,* Lixia Sun,2,* Wen Wen,1,* Jie Wang,3 Chun-yi Wang,1 Jie Ni,1 Jing-jie Jiang,1 Zhan-Hui Feng,4 Yong-Ran Cheng5 1Metabolic Disease Center, Affiliated Hospital of Hangzhou Normal University, Hangzhou, People’s Republic of China; 2Zhejiang University of Water Resources and Electric Power, Hangzhou, People’s Republic of China; 3Hangzhou Zhenqi Technology Co., Ltd, Hangzhou, People’s Republic of China; 4Neurological Department, Affiliated Hospital of Guizhou Medical University, Guiyang, People’s Republic of China; 5School of Public Health, Hangzhou Medical College, Hangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhan-Hui Feng; Yong-Ran Cheng, Email h9450203@126.com; chengyr@zjams.com.cnBackground: Many studies have shown that the pollution of fine particles in the air is related to the incidence of chronic diseases. However, research on air pollution and metabolism-associated fatty liver disease (MAFLD) is limited.Objective: The purpose of this study was to explore the relationship between short-term ambient air pollution and daily outpatient visits for metabolic-related fatty liver.Methods: We used a quasi-Poisson regression generalized additive model to stratify analyses by season, age, and gender.Results: From January 1, 2017, to August 31, 2019, 10,562 confirmed MAFLD outpatient visits were recorded. A 10 μg/m3 increase of fine particular matter (PM10and PM2.5) and NO2 concentrations corresponding with percent change were 0.82 (95% confidence interval [CI], 0.49– 1.15), 0.57 (95% CI, 0.18– 0.98), and 0.86 (95% CI, 0.59– 1.13) elevation in MAFLD outpatient visits. In terms of season, the impact estimates of NO2 and PM2.5% change were 3.55 (95% CI, 1.23– 5.87) and 1.12 (95% CI, 0.78– 1.46) in the hot season and transition season, respectively. Compared with the warm season, the impact estimates of PM10were more significant in the cool season: 2.88 (95% CI, 0.66– 5.10). NO2 has the greatest effect in the transition season, whereas PM10 has the greatest highest effect in the cool and hot seasons. Compared with other pollutants, PM2.5 has the greatest impact in the age stratification, which percent change are 2.69 (95% CI, 0.77– 5.61) and 2.88 (95% CI, 0.37– 6.40) respectively. The impact values of PM2.5 in male and female percent change were 3.60 (95% CI, 0.63– 6.57) and 1.65 (95% CI, 1.05– 2.25), respectively.Conclusion: This study shows that the air pollutants are related to the number of outpatient visits for MAFLD. The effects of different air pollutants on MAFLD outpatient visits were different by season, ages, and gender.Keywords: air pollutants, metabolism-associated fatty liver disease, generalized additive model

وصف الملف: electronic resource

Relation: https://www.dovepress.com/explore-the-relationship-between-short-term-ambient-air-pollution-expo-peer-reviewed-fulltext-article-RMHP; https://doaj.org/toc/1179-1594

URL الوصول: https://doaj.org/article/16728446afe2497e8b9e3de86b0464d9

المؤلفون: Luo L, Wen W, Wang CY, Zhou M, Ni J, Jiang J, Chen J, Wang MW, Feng Z, Cheng YR

المصدر: Risk Management and Healthcare Policy, Vol Volume 14, Pp 4393-4399 (2021)

مصطلحات موضوعية: covid-19, sars-cov-2, spreads, travel, non-linear exponential, Public aspects of medicine, RA1-1270

الوصف: Lin Luo,1– 3 Wen Wen,4 Chun-yi Wang,4 Mengyun Zhou,5 Jie Ni,6 Jingjie Jiang,4 Juan Chen,4 Ming-wei Wang,4 Zhanhui Feng,6 Yong-Ran Cheng7 1Hangzhou Ruolin Hospital Management Co. Ltd, Hangzhou, 310007, People’s Republic of China; 2Hangzhou Kaihong Technology Co., Ltd, Hangzhou, 310059, People’s Republic of China; 3Jiangxi Key Laboratory of Natural Products and Functional Food, College of Food Science and Engineering, Jiangxi Agricultural University, Nanchang, 330045, People’s Republic of China; 4Hangzhou Institute of Cardiovascular Diseases, Hangzhou Medical Key Discipline, Affiliated Hospital of Hangzhou Normal University, Hangzhou, 310015, People’s Republic of China; 5Department of Molecular & Cellular Physiology, Shinshu University School of Medicine, Nagano, 3900803, Japan; 6Internal Medicine-Neurology, Affiliated Hospital of Guizhou Medical University, Guiyang, People’s Republic of China; 7School of Public Health, Hangzhou Medical College, Hangzhou, 311300, People’s Republic of ChinaCorrespondence: Yong-Ran Cheng; Zhanhui Feng Email chengyr@zjams.com.cn; h9450203@126.comBackground and Aim: Relevant studies show that population migration has a great impact on the early spread of infectious diseases. Therefore, it is important to explore whether there is an explicit relationship between population migration and the number of confirmed cases for the control of the COVID-19 epidemic. This paper mainly explores the impact of population migration on early COVID-19 transmission, and establishes a predictive nonlinear mathematical model to predict the number of early cases.Methods: Data of confirmed cases were sourced from the official website of the Municipal Health Committee, and the proportions of migration from Wuhan to other cities were sourced from the Baidu data platform. The data of confirmed cases and the migration proportions of 14 cities in Hubei Province were collected, the COVID-19 cases study period was determined as 10 days based on the third quartile of the interval of the incubation period, and a non-linear mathematical model was constructed to clarify the relationship between the migration proportion and the number of confirmed COVID-19 cases. Finally, eight typical regions were selected to verify the accuracy of the model.Results: The daily population migration rates and the growth curves of the number of confirmed cases in the 14 cities were basically consistent, and Pearson’s correlation coefficient was 0.91. The specific mathematical expression of 14 regions is . In each of the fourteen cities, The nonlinear exponential model structure is as follows:. It was found that the R2 values of the fitted mathematical model were greater than 0.8 in all studied regions, excluding Suizhou (p < 0.05). The established mathematical model was used to fit eight regions in China, and the correlations between the predicted and actual numbers of confirmed cases were greater than 0.9, excluding that of Hebei Province (0.82).Conclusion: The study found that population migration has a positive and significant impact on the spread of COVID-19. Modeling COVID-19 risk may be a useful strategy for directing public health surveillance and interventions. Restricting the migration of the population is of great significance to the joint prevention and control of the pandemic worldwide.Keywords: COVID-19, SARS-CoV-2, spreads, travel, non-linear exponential

وصف الملف: electronic resource

Relation: https://www.dovepress.com/exploring-the-pattern-of-early-covid-19-transmission-caused-by-populat-peer-reviewed-fulltext-article-RMHP; https://doaj.org/toc/1179-1594

URL الوصول: https://doaj.org/article/95ed72dcc72644afbfba9e4513bcee3b

المؤلفون: Wuttke, M, Li, Y, Li, M, Sieber, KB, Feitosa, MF, Gorski, M, Tin, A, Wang, LH, Chu, AY, Hoppmann, A, Kirsten, H, Giri, A, Chai, JF, Sveinbjornsson, G, Tayo, BO, Nutile, T, Fuchsberger, C, Marten, J, Cocca, M, Ghasemi, S, Xu, YZ, Horn, K, Noce, D, Van der Most, PJ, Sedaghat, S, Yu, Z, Akiyama, M, Afaq, S, Ahluwalia, TS, Almgren, P, Amin, N, Arnlo, J, Bakker, SJL, Bansal, N, Baptista, D, Bergmann, S, Biggs, ML, Biino, G, Boehnke, M, Boerwinkle, E, Boissel, M, Bottinger, EP, Boutin, TS, Brenner, H, Brumat, M, Burkhardt, R, Butterworth, AS, Campbell, A, Campbell, H, Canouil, M, Carroll, RJ, Catamo, E, Chambers, JC, Chee, ML, Chen, X, Cheng, CY, Cheng, YR, Christensen, K, Cifkova, R, Ciullo, M, Concas, MP, Cook, JP, Coresh, J, Corre, T, Sala, CF, Cusi, D, Danesh, J, Daw, EW, Borst, MH, Grandi, A, Mutsert, R, Vries, APJ, Degenhardt, F, Delgado, G, Demirkan, A, Di Angelantonio, E, Dittrich, K, Divers, J, Dorajoo, R, Eckardt, KU, Ehret, G, Elliott, P, Endlich, K, Evans, MK, Felix, JF, Foo, VHX, Franco, OH, Franke, A, Freedman, BI, Freitag-Wolf, S, Friedlander, Y, Froguel, P, Gansevoort, RT, Gao, H, Gasparini, P, Gaziano, JM, Giedraitis, V, Gieger, C, Girotto, G, Giulianini, F, Gogele, M, Gordon, SD, Gudbjartsson, DF, Gudnason, V, Haller, T, Hamet, P, Harris, TB, Hartman, CA, Hayward, C, Hellwege, JN, Heng, CK, Hicks, AA, Hofer, E, Huang, W, Hutri-Kahonen, N, Hwang, SJ, Ikram, MA, Indridason, OS, Ingelsson, E, Ising, M, Jaddoe, VWV, Jakobsdottir, J, Jonas, JB, Joshi, PK, Josyula, NS, Jung, B, Kahonen, M, Kamatani, Y, Kammerer, CM, Kanai, M, Kastarinen, M, Kerr, SM, Khor, CC, Kiess, W, Kleber, ME, Koenig, W, Kooner, JS, Korner, A, Kovacs, P, Kraja, AT, Krajcoviechova, A, Kramer, H, Kramer, BK, Kronenberg, F, Kubo, M, Kuhnel, B, Kuokkanen, M, Kuusisto, J, Bianca, M, Laakso, M, Lange, LA, Langefeld, CD, Lee, JJM, Lehne, B, Lehtimaki, T, Lieb, W, Lim, SC, Lind, L, Lindgren, CM, Liu, J, Liu, JJ, Loeffler, M, Loos, RJF, Lucae, S, Lukas, MA, Lyytikainen, LP, Magi, R, Magnusson, PKE, Mahajan, A, Martin, NG, Martins, J, Marz, W, Mascalzoni, D, Matsuda, K, Meisinger, C, Meitinger, T, Melander, O, Metspalu, A, Mikaelsdottir, EK, Milaneschi, Y, Miliku, K, Mishra, PP, Program, VAMV, Mohlke, KL, Mononen, N, Montgomery, GW, Mook-Kanamori, DO, Mychaleckyj, JC, Nadkarni, GN, Nalls, MA, Nauck, M, Nikus, K, Ning, B, Nolte, IM, Noordam, R, O'Connell, J, O'Donoghue, ML, Olafsson, I, Oldehinkel, AJ, Orho-Melander, M, Ouwehand, WH, Padmanabhan, S, Palmer, ND, Palsson, R, Penninx, BWJH, Perls, T, Perola, M, Pirastu, M, Pirastu, N, Pistis, G, Podgornaia, AI, Polasek, O, Ponte, B, Porteous, DJ, Poulain, T, Pramstaller, PP, Preuss, MH, Prins, BP, Province, MA, Rabelink, TJ, Raffield, LM, Raitakari, OT, Reilly, DF, Rettig, R, Rheinberger, M, Rice, KM, Ridker, PM, Rivadeneira, F, Rizzi, F, Roberts, DJ, Robino, A, Rossing, P, Rudan, I, Rueedi, R, Ruggiero, D, Ryan, KA, Saba, Y, Sabanayagam, C, Salomaa, V, Salvi, E, Saum, KU, Schmidt, H, Schmidt, R, Schottker, Schulz, CA, Schupf, N, Shaffer, CM, Shi, Y, Smith, AV, Smith, BH, Soranzo, N, Spracklen, CN, Strauch, K, Stringham, HM, Stumvoll, M, Svensson, PO, Szymczak, S, Tai, ES, Tajuddin, SM, Tan, NYQ, Taylor, KD, Teren, A, Tham, YC, Thiery, J, Thio, CHL, Thomsen, H, Thorleifsson, G, Toniolo, D, Tonjes, A, Tremblay, J, Tzoulaki, I, Uitterlinden, AG, Vaccargiu, S, Van Dam, RM, Harst, PD, Duijn, CM, Edward, DRV, Verweij, N, Vogelezang, S, Volker, U, Vollenweider, P, Waeber, G, Waldenberger, M, Wallentin, L, Wang, YX, Wang, C, Waterworth, DM, Wei, W, White, H, Whitfield, JB, Wild, SH, Wilson, JF, Wojczynski, MK, Wong, C, Wong, TY, Xu, L, Yang, Q, Yasuda, M, Yerges-Armstrong, LM, Zhang, WH, Zonderman, AB, Rotter, JI, Bochud, M, Psaty, BM, Vitart, V, Wilson, JG, Dehghan, A, Parsa, A, Chasman, DI, Ho, K, Morris, AP, Devuyst, O, Akilesh, S, Pendergrass, SA, Sim, XL, Boger, CA, Okada, Y, Edwards, TL, Snieder, H, Stefansson, K, Hung, AM, Heid, IM, Scholz, M, Teumer, A, Kottgen, A, Pattaro, C

المصدر: Nature genetics. 51(6):957

مصطلحات موضوعية: Medicin och hälsovetenskap

URL الوصول: http://kipublications.ki.se/Default.aspx?queryparsed=id:141084252

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