Evidence map›Paper›PMID 32293008›Full record

ReviewInternational journal of epidemiology2020

Advances in spatiotemporal models for non-communicable disease surveillance.

Marta Blangiardo, Areti Boulieri, Peter Diggle, Frédéric B Piel, Gavin Shaddick, Paul Elliott

Abstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in International journal of epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
19citing papers in PubMed, 2 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

19 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Area-Level Determinants in Colorectal Cancer Spatial Clustering Studies: A Systematic Review.International journal of environmental research and public health · 2021
    Pooled it
  2. A Systematic Review of Spatial and Spatio-temporal Analyses in Public Health Research in Korea.Journal of preventive medicine and public health = Yebang Uihakhoe chi · 2021
    Pooled it
  3. Spatio-temporal inequalities in vulvar cancer mortality in Spain, 1999-2023: a nationwide Bayesian analysis.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2026
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  11. Risk Factors for Locomotive Crew Members Depending on Their Place of Work.International journal of environmental research and public health · 2022
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  17. Small-area methods for investigation of environment and health.International journal of epidemiology · 2020
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Marta BlangiardoUK Small Area Health Statistics Unit.
Areti BoulieriMRC-PHE Centre for Environment & Health, Department of Epidemiology & Biostatistics, Imperial College London, London, UK.
Peter DiggleCentre for Health Informatics, Computing and Statistics (CHICAS), Lancaster Medical School, Lancaster University, Lancaster, UK.
Frédéric B PielUK Small Area Health Statistics Unit.
Gavin ShaddickDepartment of Mathematics, University of Exeter, Exeter, UK.
Paul ElliottUK Small Area Health Statistics Unit.

Funding

Medical Research Council MR/L01341X/1Wellcome TrustWellcome Trust 204535/Z/16/Z
6 · The paper itself

Abstract

Surveillance systems are commonly used to provide early warning detection or to assess an impact of an intervention/policy. Traditionally, the methodological and conceptual frameworks for surveillance have been designed for infectious diseases, but the rising burden of non-communicable diseases (NCDs) worldwide suggests a pressing need for surveillance strategies to detect unusual patterns in the data and to help unveil important risk factors in this setting. Surveillance methods need to be able to detect meaningful departures from expectation and exploit dependencies within such data to produce unbiased estimates of risk as well as future forecasts. This has led to the increasing development of a range of space-time methods specifically designed for NCD surveillance. We present an overview of recent advances in spatiotemporal disease surveillance for NCDs, using hierarchically specified models. This provides a coherent framework for modelling complex data structures, dealing with data sparsity, exploiting dependencies between data sources and propagating the inherent uncertainties present in both the data and the modelling process. We then focus on three commonly used models within the Bayesian Hierarchical Model (BHM) framework and, through a simulation study, we compare their performance. We also discuss some challenges faced by researchers when dealing with NCD surveillance, including how to account for false detection and the modifiable areal unit problem. Finally, we consider how to use and interpret the complex models, how model selection may vary depending on the intended user group and how best to communicate results to stakeholders and the general public.

Indexed as

Noncommunicable DiseasesPopulation SurveillanceBayes TheoremHumansSpatio-Temporal AnalysisBayesian hierarchical modelsnon-communicable diseasesspattemporal modellingSurveillance

Identifiers

PMID32293008
PMCPMC7158067

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.