1
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Bayesian spatio-temporal distributed lag modeling for delayed climatic effects on sparse malaria incidence data

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Background

          In many areas of the Greater Mekong Subregion (GMS), malaria endemic regions have shrunk to patches of predominantly low-transmission. With a regional goal of elimination by 2030, it is important to use appropriate methods to analyze and predict trends in incidence in these remaining transmission foci to inform planning efforts. Climatic variables have been associated with malaria incidence to varying degrees across the globe but the relationship is less clear in the GMS and standard methodologies may not be appropriate to account for the lag between climate and incidence and for locations with low numbers of cases.

          Methods

          In this study, a methodology was developed to estimate the spatio-temporal lag effect of climatic factors on malaria incidence in Thailand within a Bayesian framework. A simulation was conducted based on ground truth of lagged effect curves representing the delayed relation with sparse malaria cases as seen in our study population. A case study to estimate the delayed effect of environmental variables was used with malaria incidence at a fine geographic scale of sub-districts in a western province of Thailand.

          Results

          From the simulation study, the model assumptions which accommodated both delayed effects and excessive zeros appeared to have the best overall performance across evaluation metrics and scenarios. The case study demonstrated lagged climatic effect estimation of the proposed modeling with real data. The models appeared to be useful to estimate the shape of association with malaria incidence.

          Conclusions

          A new method to estimate the spatiotemporal effect of climate on malaria trends in low transmission settings is presented. The developed methodology has potential to improve understanding and estimation of past and future trends in malaria incidence. With further development, this could assist policy makers with decisions on how to more effectively distribute resources and plan strategies for malaria elimination.

          Related collections

          Most cited references52

          • Record: found
          • Abstract: found
          • Article: found
          Is Open Access

          Distributed lag non-linear models

          Environmental stressors often show effects that are delayed in time, requiring the use of statistical models that are flexible enough to describe the additional time dimension of the exposure–response relationship. Here we develop the family of distributed lag non-linear models (DLNM), a modelling framework that can simultaneously represent non-linear exposure–response dependencies and delayed effects. This methodology is based on the definition of a ‘cross-basis’, a bi-dimensional space of functions that describes simultaneously the shape of the relationship along both the space of the predictor and the lag dimension of its occurrence. In this way the approach provides a unified framework for a range of models that have previously been used in this setting, and new more flexible variants. This family of models is implemented in the package dlnm within the statistical environment R. To illustrate the methodology we use examples of DLNMs to represent the relationship between temperature and mortality, using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) for New York during the period 1987–2000. Copyright © 2010 John Wiley & Sons, Ltd.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Bayesian image restoration, with two applications in spatial statistics

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Distributed Lag Linear and Non-Linear Models in R: The Package dlnm.

              Distributed lag non-linear models (DLNMs) represent a modeling framework to flexibly describe associations showing potentially non-linear and delayed effects in time series data. This methodology rests on the definition of a crossbasis, a bi-dimensional functional space expressed by the combination of two sets of basis functions, which specify the relationships in the dimensions of predictor and lags, respectively. This framework is implemented in the R package dlnm, which provides functions to perform the broad range of models within the DLNM family and then to help interpret the results, with an emphasis on graphical representation. This paper offers an overview of the capabilities of the package, describing the conceptual and practical steps to specify and interpret DLNMs with an example of application to real data.
                Bookmark

                Author and article information

                Contributors
                chawarat.rot@mahidol.ac.th
                Journal
                BMC Med Res Methodol
                BMC Med Res Methodol
                BMC Medical Research Methodology
                BioMed Central (London )
                1471-2288
                20 December 2021
                20 December 2021
                2021
                : 21
                : 287
                Affiliations
                [1 ]GRID grid.10223.32, ISNI 0000 0004 1937 0490, Department of Tropical Hygiene, Faculty of Tropical Medicine, , Mahidol University, ; Ratchathewi, Bangkok, 10400 Thailand
                [2 ]GRID grid.10223.32, ISNI 0000 0004 1937 0490, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, , Mahidol University, ; Bangkok, Thailand
                [3 ]GRID grid.415836.d, ISNI 0000 0004 0576 2573, Division of Vector Borne Diseases, Department of Disease Control, Ministry of Public Health, ; Nonthaburi, Thailand
                [4 ]GRID grid.38142.3c, ISNI 000000041936754X, Harvard T.H. Chan School of Public Health, , Harvard University, ; Cambridge, MA USA
                [5 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, , University of Oxford, ; Oxford, UK
                [6 ]GRID grid.10837.3d, ISNI 0000000096069301, The Open University, ; Milton Keynes, UK
                Article
                1480
                10.1186/s12874-021-01480-x
                8690908
                34930128
                3705f56d-61df-432f-8828-78283f2093af
                © The Author(s) 2021

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 31 May 2021
                : 22 November 2021
                Categories
                Research
                Custom metadata
                © The Author(s) 2021

                Medicine
                spatiotemporal,malaria,bayesian,lag effect,weather
                Medicine
                spatiotemporal, malaria, bayesian, lag effect, weather

                Comments

                Comment on this article