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
  • [41] SPATIO-TEMPORAL REGULARIZATION OF FLOW-FIELDS
    Bostan, Emrah
    Vardoulis, Orestis
    Piccini, Davide
    Tafti, Pouya Dehghani
    Stergiopulos, Nikolaos
    Unser, Michael
    2013 IEEE 10TH INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING (ISBI), 2013, : 836 - 839
  • [42] Spatio-temporal optical random number generator
    Stipcevic, M.
    Bowers, J. E.
    OPTICS EXPRESS, 2015, 23 (09): : 11619 - 11631
  • [43] QUADRATIC VARIATION OF GAUSSIAN RANDOM FIELDS
    DEO, CM
    WONG, SF
    THEORY OF PROBABILITY AND ITS APPLICATIONS, 1978, 23 (03) : 628 - 633
  • [44] Scale-space spatio-temporal random fields: Application to the detection of growing microbial patterns from surface roughness
    Ahmad, Ola
    Collet, Christophe
    PATTERN RECOGNITION, 2016, 58 : 27 - 38
  • [45] Comparison between spatio-temporal random processes and application to climate model data
    Li, Bo
    Zhang, Xianyang
    Smerdon, Jason E.
    ENVIRONMETRICS, 2016, 27 (05) : 267 - 279
  • [46] Sparse Spatio-temporal Gaussian Processes with General Likelihoods
    Hartikainen, Jouni
    Riihimaki, Jaakko
    Sarkka, Simo
    ARTIFICIAL NEURAL NETWORKS AND MACHINE LEARNING - ICANN 2011, PT I, 2011, 6791 : 193 - 200
  • [47] Spatio-temporal Gaussian Mixture Model for Background Modeling
    Soh, Youngsung
    Hae, Yongsuk
    Kim, Intaek
    2012 IEEE INTERNATIONAL SYMPOSIUM ON MULTIMEDIA (ISM), 2012, : 360 - 363
  • [48] Gaussian Process-based Spatio-Temporal Predictor
    Varga, Balazs
    ACTA POLYTECHNICA HUNGARICA, 2022, 19 (05) : 69 - 84
  • [49] Spatio-temporal variation in the Citrullus colocynthis microbiome
    Procter, M.
    Kundu, B.
    Sudalaimuthuasari, N.
    Al Meskari, R. S.
    Hazzouri, K. M.
    Amiri, K. M. A.
    MOLECULAR PLANT-MICROBE INTERACTIONS, 2024, 37 (05) : 25 - 25
  • [50] Spatio-Temporal Variation of Conversational Utterances on Twitter
    Alis, Christian M.
    Lim, May T.
    PLOS ONE, 2013, 8 (10):