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Hong Y, Ning L, Sun Y, Qian H, Ji Y. The growth and shape of the eyeball and crystalline lens in utero documented by fetal MR imaging. Heliyon 2023; 9:e12885. [PMID: 36685428 PMCID: PMC9851875 DOI: 10.1016/j.heliyon.2023.e12885] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2022] [Revised: 01/06/2023] [Accepted: 01/06/2023] [Indexed: 01/11/2023] Open
Abstract
Purpose To study the growth model, shape, and developmental relationship of lens and eyeball, we used two-dimensional Magnetic Resonance (MR) imaging to investigate gestationally age-related changes in the selected ocular parameters in vivo. Materials and methods We retrospectively reviewed the MR images from 126 fetuses ranging from 21 to 39 weeks' gestation. Ocular parameters on MR imaging of transverse plane were measured including lens diameter (LD), anteroposterior lens diameter (APLD), lens surface area (LS), globe diameter (GD), anteroposterior globe diameter (APGD), globe surface area (GS). The growth model of each biometric against gestational age (GA), aspect ratio of lens and globe (LD/APLD and GD/APGD), and growing relationship between the ratio of lens and globe surface area (LS/GS) were studied by statistical analysis. Results The growth model of most biometry for gestational age is logarithmic, except for the diameter of the ocular globe (GD and APGD) showing a quadratic growth pattern. Our study showed that the lens was consistently larger in the transverse than the anteroposterior diameters during 21-39 weeks(P < 0.001). Besides, the ratio of surface area (LS/GS) was not significantly changing with GA(P = 0.4908), while the increase of LS was significantly accorded with that of GS(P < 0.001). Conclusion The lens shape throughout fetal life may take part in the process, shape changing from vertical ellipsoid, spherical to transversal ellipsoid, based on the logarithmically increased ratio of lens transverse and anteroposterior diameters. In the meanwhile, the aspect ratio of eyeball in late fetal life may imply a gradually spherical shape during gestation. Nomogram data from this study may provide appropriate information about morphological changes in the fetal lens and the synchronous relationship between lens and eyeball.
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Key Words
- AIC, Akaike Information Criterion
- APGD, anteroposterior globe diameter
- APLD, anteroposterior lens diameter
- CC, correlation coefficient
- CI, confidence intervals
- Eye biometry
- Fetus
- GA, gestational age
- GD, globe diameter
- GS, globe surface area
- LD, lens diameter
- LS, lens surface area
- Lens growth
- Lens shape
- MR imaging
- MR, Magnetic Resonance
- OLS, ordinary least square
- Ocular globe growth
- SD, standard deviation
- SNR, signal-to noise ratio
- T2W, T2 weighted
- US, ultrasound
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Affiliation(s)
- Yingying Hong
- Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, 200031, China,NHC Key Laboratory of Myopia (Fudan University), Key Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, 200031, China,Shanghai Key Laboratory of Visual Impairment and Restoration, Shanghai, 200031, China
| | - Li Ning
- Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, 200031, China,NHC Key Laboratory of Myopia (Fudan University), Key Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, 200031, China,Shanghai Key Laboratory of Visual Impairment and Restoration, Shanghai, 200031, China
| | - Yang Sun
- Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, 200031, China,NHC Key Laboratory of Myopia (Fudan University), Key Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, 200031, China,Shanghai Key Laboratory of Visual Impairment and Restoration, Shanghai, 200031, China
| | - Huijun Qian
- Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, Shanghai, 200011, China,Corresponding author. Department of Radiology, Obstetrics and Gynecology Hospital, Fudan University, No. 419 Fangxie Rd. Shanghai, 200011, China.
| | - Yinghong Ji
- Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, Shanghai, 200031, China,NHC Key Laboratory of Myopia (Fudan University), Key Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai, 200031, China,Shanghai Key Laboratory of Visual Impairment and Restoration, Shanghai, 200031, China,Corresponding author. Eye Institute and Department of Ophthalmology, Eye & ENT Hospital, Fudan University, NHC Key Laboratory of Myopia (Fudan University), Key Laboratory of Myopia, Chinese Academy of Medical Sciences, Shanghai Key Laboratory of Visual Impairment and Restoration, No. 83 Fenyang Road, Shanghai, 200031, China.
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Wang W, Liu Y, Ye P, Xu C, Qiu Y, Yin P, Liu J, Qi J, You J, Lin L, Wang L, Li J, Shi W, Zhou M. Spatial variations and social determinants of life expectancy in China, 2005-2020: A population-based spatial panel modelling study. Lancet Reg Health West Pac 2022; 23:100451. [PMID: 35465044 PMCID: PMC9019400 DOI: 10.1016/j.lanwpc.2022.100451] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
BACKGROUND Social determinants of health (SDOH) produce a broad range of life expectancy (LE) disparities. In China, limited literatures were found to report association between SDOH and LE at ecological level during a consecutive period of time from the spatial perspectives. This study aimed to determine the existence, quantify the magnitude, and interpret the association between SDOH and LE in China. METHODS Provincial-level LE were estimated from mortality records during 2005-2020 from National Mortality Surveillance System in China. A spatial panel Durbin model was used to investigate LE associated SDOH proxies. Spatial spillover effects were introduced to interpret direct and indirect effects caused by SDOH during long-term and short-term period on LE disparities. FINDINGS Nationwide, LE increased from 73.1 (95% confidence interval (CI): 71.3, 74.4) years to 77.7 (95%CI: 76.5, 78.7) years from 2005 to 2020. Unequally spatial distribution of LE with High-High clustering in coastal areas and Low-Low clustering in western regions were observed. Locally, it was estimated that SDOH proxies statistically significant related to an increase of LE, including GDP (coefficient: 0.02, 95%CI: 0.00, 0.03), Gini index (coefficient: 2.35, 95%CI: 1.82, 2.88), number of beds in health care institutions (coefficient: 0.02, 95%CI: 0.00, 0.05) and natural growth rate of resident population (coefficient: 0.02, 95%CI: 0.01, 0.02). Direct and indirect effects decomposition during long-term and short-term of LE associated SDOH proxies demonstrated that GDP, urbanization rate, unemployment rate, education attainment, Gini index, number of beds in health care institutions, sex ratio, gross dependence ratio and natural growth rate of resident population not only affected local LE, but also exerted spatial spillover effects towards geographical neighbors. INTERPRETATION Spatial variations of LE existed at provincial-level in China. SDOH regarding socioeconomic development and equity, healthcare resources, as well as population characteristics not only affected LE disparities at local scale but also among nearby provinces. Externalities of policy of those SDOH proxies should be took into consideration to promote health equity nationally. Comprehensive approaches on the basis of population strategy should be consolidated to optimize supportive socioeconomic environment and narrow the regional gap to reduce health disparities and increase LE. FUNDING National Key Research & Development Program of China (Grant No.2018YFC1315301); Ministry of Education of China Humanities and Social Science General Program (Grant No.18YJC790138).
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Key Words
- AIC, Akaike Information Criterion
- CI, confidence interval
- China
- DSPs, Disease Surveillance Points system
- LE, life expectancy
- LM test, Lagrange Multiplier test
- LR, Likelihood ratio
- Life expectancy
- NMSS, National Mortality Surveillance System
- OLS, ordinary least square
- Population strategy
- SBIC, Schwarz's Bayesian Information Criterion
- SD, standard deviation
- SDOH, social determinants of health
- SPAR, spatial panel autoregressive regression model
- SPDM, spatial panel Durbin model
- SPEM, spatial panel error model
- Social determinants of health
- Spatial spillover effects
- Spatial variations
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Affiliation(s)
- Wei Wang
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Yunning Liu
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Pengpeng Ye
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Chengdong Xu
- Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Science, Beijing, China
| | - Yun Qiu
- Institute for Economic and Social Research, Jinan University, Guangzhou, Guangdong, China
| | - Peng Yin
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jiangmei Liu
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jinlei Qi
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Jinling You
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Lin Lin
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Lijun Wang
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
| | - Junming Li
- School of Statistics, Shanxi University of Finance and Economics, Taiyuan, Shanxi, China
| | - Wei Shi
- Institute for Economic and Social Research, Jinan University, Guangzhou, Guangdong, China
| | - Maigeng Zhou
- National Center for Chronic and Noncommunicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, China
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Rijnhart JJM, Twisk JWR, Chinapaw MJM, de Boer MR, Heymans MW. Comparison of methods for the analysis of relatively simple mediation models. Contemp Clin Trials Commun 2017; 7:130-5. [PMID: 29696178 DOI: 10.1016/j.conctc.2017.06.005] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2017] [Revised: 06/19/2017] [Accepted: 06/20/2017] [Indexed: 11/30/2022] Open
Abstract
Background/aims Statistical mediation analysis is an often used method in trials, to unravel the pathways underlying the effect of an intervention on a particular outcome variable. Throughout the years, several methods have been proposed, such as ordinary least square (OLS) regression, structural equation modeling (SEM), and the potential outcomes framework. Most applied researchers do not know that these methods are mathematically equivalent when applied to mediation models with a continuous mediator and outcome variable. Therefore, the aim of this paper was to demonstrate the similarities between OLS regression, SEM, and the potential outcomes framework in three mediation models: 1) a crude model, 2) a confounder-adjusted model, and 3) a model with an interaction term for exposure-mediator interaction. Methods Secondary data analysis of a randomized controlled trial that included 546 schoolchildren. In our data example, the mediator and outcome variable were both continuous. We compared the estimates of the total, direct and indirect effects, proportion mediated, and 95% confidence intervals (CIs) for the indirect effect across OLS regression, SEM, and the potential outcomes framework. Results OLS regression, SEM, and the potential outcomes framework yielded the same effect estimates in the crude mediation model, the confounder-adjusted mediation model, and the mediation model with an interaction term for exposure-mediator interaction. Conclusions Since OLS regression, SEM, and the potential outcomes framework yield the same results in three mediation models with a continuous mediator and outcome variable, researchers can continue using the method that is most convenient to them.
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