Variation in use of Caesarean section in Norway: An application of spatio-temporal Gaussian random fields

被引:2
|
作者
Mannseth, Janne [1 ]
Berentsen, Geir D. [2 ]
Skaug, Hans J. [3 ]
Lie, Rolv T. [1 ]
Moster, Dag [1 ]
机构
[1] Univ Bergen, Dept Global Publ Hlth & Primary Care, Arstadveien 17, N-5020 Bergen, Vestland, Norway
[2] Norwegian Sch Econ, Dept Business & Management Sci, Bergen, Norway
[3] Univ Bergen, Dept Math, Bergen, Norway
关键词
Spatial correlation; geographical variation; Caesarean section; SPDE; INLA; TMB;
D O I
10.1177/14034948211008579
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
Aims: Caesarean section (CS) is a medical intervention performed in Norway when a surgical delivery is considered more beneficial than a vaginal. Because deliveries with higher risk are centralized to larger hospitals, use of CS varies considerably between hospitals. We describe how the use of CS varies geographically by municipality. Since indications for CS should have little variation across the relatively homogenous population of Norway, we expect fair use of CS to be evenly distributed across the municipalities. Methods: Data from the Medical Birth Registry of Norway were used in our analyses (810,914 total deliveries, 133,746 CSs, 440 municipalities). We propose a spatial correlation model that takes the location into account to describe the variation in use of CS across the municipalities. The R packages R-INLA and TMB are used to estimate the yearly municipal CS rate and the spatial correlation between the municipalities. We also apply stratified models for different categories of delivering women (Robson groups). Estimated rates are displayed in maps and model parameters are shown in tables. Results: The CS rate varies substantially between the different municipalities. As expected, there was strong correlation between neighbouring municipalities. Similar results were found for different Robson groups. Conclusions: The substantial difference in CS use across municipalities in Norway is not likely to be due to specific medical reasons, but rather to hospitals' different policies towards the use of CS. The policy to be either more or less restrictive to CS was not specific to any category of deliveries.
引用
收藏
页码:891 / 898
页数:8
相关论文
共 50 条
  • [11] Predicting spatio-temporal random fields: Some computational aspects
    De Iaco, S.
    Posa, D.
    COMPUTERS & GEOSCIENCES, 2012, 41 : 12 - 24
  • [12] Spatio-temporal fMRI analysis using Markov random fields
    Descombes, X
    Kruggel, F
    von Cramon, DY
    IEEE TRANSACTIONS ON MEDICAL IMAGING, 1998, 17 (06) : 1028 - 1039
  • [13] ON SOME MATERN COVARIANCE FUNCTIONS FOR SPATIO-TEMPORAL RANDOM FIELDS
    Ip, Ryan H. L.
    Li, W. K.
    STATISTICA SINICA, 2017, 27 (02) : 805 - 822
  • [14] Spatio-temporal random fields: compressible representation and distributed estimation
    Piatkowski, Nico
    Lee, Sangkyun
    Morik, Katharina
    MACHINE LEARNING, 2013, 93 (01) : 115 - 139
  • [15] Deep Spatio-Temporal Random Fields for Efficient Video Segmentation
    Chandra, Siddhartha
    Couprie, Camille
    Kokkinos, Iasonas
    2018 IEEE/CVF CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION (CVPR), 2018, : 8915 - 8924
  • [16] Spatio-temporal random fields: compressible representation and distributed estimation
    Nico Piatkowski
    Sangkyun Lee
    Katharina Morik
    Machine Learning, 2013, 93 : 115 - 139
  • [17] Spatio-Temporal Variational Gaussian Processes
    Hamelijnck, Oliver
    Wilkinson, William J.
    Loppi, Niki A.
    Solin, Arno
    Damoulas, Theodoros
    ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 34 (NEURIPS 2021), 2021,
  • [18] Spatio-Temporal Event Detection Using Dynamic Conditional Random Fields
    Yin, Jie
    Hu, Derek Hao
    Yang, Qiang
    21ST INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE (IJCAI-09), PROCEEDINGS, 2009, : 1321 - 1326
  • [19] Test and Visualization of Covariance Properties for Multivariate Spatio-Temporal Random Fields
    Huang, Huang
    Sun, Ying
    Genton, Marc G.
    JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS, 2023, 32 (04) : 1545 - 1555
  • [20] A TEST FOR STATIONARITY OF SPATIO-TEMPORAL RANDOM FIELDS ON PLANAR AND SPHERICAL DOMAINS
    Jun, Mikyoung
    Genton, Marc G.
    STATISTICA SINICA, 2012, 22 (04) : 1737 - 1764