Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data

Kang, Su Yun; Cramb, Susanna; White, Nicole; Ball, Stephen; Mengersen, Kerrie

doi:10.4081/gh.2016.428

Cited by 40 publications

(43 citation statements)

References 55 publications

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“…an entire state (Anselin et al 2006a). This smoothing method adjusts rates toward the overall mean, reduces variance instability, and produces robust and reliable rate estimates even for small samples (Kang et al 2016;Mollalo et al 2017). Adjusted ABO rates using Empirical Bayes smoother algorithm were calculated in the GeoDa software (Anselin et al 2006b) for each census tract and these adjusted ABO rates were used in our analyses (Figure 1).…”

Section: Methodsmentioning

confidence: 99%

Spatial patterns of adverse birth outcomes among black and white women in Massachusetts – the role of population-level and individual-level factors

Ogneva‐Himmelberger

Haynes

2020

GES

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This study explores spatial distribution of adverse birth outcomes (ABO), defined as low birth weight (<=2500 g) and preterm deliveries (gestational age <37 weeks), in black and white mothers in the state of Massachusetts, USA. It uses 817877 individual birth records from 2000-2014 aggregated to census tracts (census enumeration unit with population of approximately 4500 people). To account for small numbers of births in some tracts, an Empirical Bayes smoother algorithm is used to calculate ABO rates. The study applies ordinary least squares (OLS) and spatial regression to examine the relationship between ABO rates, seven individual-level factors from birth certificates and nine population-level factors (income level, education level, race) from census data. Explanatory power of these factors varies between the two races. In models based only on individual-level factors, all seven factors were significant (p<0.05) in the black mothers’ model while only three were significant in the white mothers’ model. Models based only on population-level variables produced better results for the white mothers than for black mothers. Models that included both individual and population-level variables explained 40% and 29% of ABO variance for black and white women respectively. The findings from this study give health-care providers and health-care policy-makers important information regarding ABO rates and the contributing factors at a local level, thus enabling them to isolate specific areas with the highest need for targeted interventions.

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Section: Methodsmentioning

confidence: 99%

Spatial patterns of adverse birth outcomes among black and white women in Massachusetts – the role of population-level and individual-level factors

Ogneva‐Himmelberger

Haynes

2020

GES

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“…Disparities in ARI cases across broad socioeconomic status and geographical regions have been reported internationally [7]. As earlier noted by [8] correct home based management is deficient and knowledge of danger symptoms was low.…”

Section: Introductionmentioning

confidence: 92%

“…Efforts to monitor and reduce ARI cases disparities can benefit greatly from quantifying variation across populations in small geographical areas like counties. An understanding of the geographic patterns of ARI can assist in improving health decision-making by health care planners like county governments to be more accurate and effective, for example by targeting policy development and resource allocation at areas of greater need [7]. This study was also carried out with the aim of establishing the distribution of prevalence of ARI across all the counties in Kenya.…”

Section: Introductionmentioning

confidence: 99%

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Determinants and Spatial Modeling of Acute Respiratory Infections (ARI) Among Children Less Than Five Years in Kenya

Muthoni¹

2017

AJTAS

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Abstract:Bayesian disease mapping is a field of statistics that is used to model the spatial distribution of disease outcomes especially in application to studies in spatial biostatistics and also as a tool to help develop the required intervention strategies. In this study, we perform a spatial modeling of ARI among children less than five years in Kenya using data from the 2014 Kenya Demographic and Health Survey (KDHS). Four models were used in this study namely the logistic regression model, the normal unstructured heterogeneity random effects model, ICAR (Integrated Conditional Autoregressive) spatial random effects model and the convolution model. A full Bayesian approach was used and the models were implemented using the Winbugs software version 1.4. Model selection was based on the DIC value where the model with the lowest DIC value was considered to be the best. The convolution model was the best model in this case and was used to map ARI across the different counties in Kenya. The national prevalence was 47.3%. The prevalence was found to be highest in the counties in the western part of Kenya. From the analysis, it's clear that ARI is still a menace that need to be controlled. Proper planning and allocation of resources need to be put in place by the county governments in order to curb the rising cases of ARI.

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“…to generate reliable posterior probability estimates, thus informing reliable map construction. [2][3][4][5][6][7][8][9] Oral cancer, primarily squamous cell carcinoma (SCC) arising from the mucosal lining of the lips, oral cavity and oropharynx, is the 14th highest malignancy worldwide in terms of both incidence and mortality. 1,10,11 With substantive geographic variation in disease distribution, proactive preventive measures and targeted screening of disease-prone individuals in "high-risk" regions are pivotal methods to reduce the high five-year mortality rates associated with late cancer detection.…”

Section: Introductionmentioning

confidence: 99%

Bayesian disease mapping and the ‘High‐Risk’ oral cancer population in Hong Kong

Adeoye

Choi

Thomson

2020

J Oral Pathology Medicine

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Background: Preventive and early diagnostic methods such as health promotion and disease screening are increasingly advocated to improve detection and survival rates for oral cancer. These strategies are most effective when targeted at "high-risk" individuals and populations. Bayesian disease-mapping modelling is a statistical method to quantify and explain spatial and temporal patterns for risk and covariate factor influence, thereby identifying "high-risk" sub-regions or "case clustering" for targeted intervention. Rarely applied to oral cancer epidemiology, this paper highlights the efficacy of disease mapping for the Hong Kong population. Methods: Following ethical approval, anonymized individual-level data for oral cancer diagnoses were obtained retrospectively from the Clinical Data Analysis and Reporting System (CDARS) of the Hong Kong Hospital Authority (HA) database for a 7-year period (January 2013 to December 2019). Data facilitated disease mapping and estimation of relative risks of oral cancer incidence and mortality. Results: A total of 3,341 new oral cancer cases and 1,506 oral cancer-related deaths were recorded during the 7-year study period. Five districts, located in Hong Kong Island and Kowloon, exhibited considerably higher relative incidence risks with 1 significant "case cluster" hotspot. Six districts displayed higher mortality risks than expected from territory-wide values, with highest risk identified for two districts of Hong Kong Island. Conclusion: Bayesian disease mapping is successful in identifying and characterizing "high-risk" areas for oral cancer incidence and mortality within a community. This should facilitate targeted preventive and interventional strategies. Further work is encouraged to enhance global-level data and comprehensive mapping of oral cancer incidence, mortality and survival.

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Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data

Cited by 40 publications

References 55 publications

Spatial patterns of adverse birth outcomes among black and white women in Massachusetts – the role of population-level and individual-level factors

Spatial patterns of adverse birth outcomes among black and white women in Massachusetts – the role of population-level and individual-level factors

Determinants and Spatial Modeling of Acute Respiratory Infections (ARI) Among Children Less Than Five Years in Kenya

Bayesian disease mapping and the ‘High‐Risk’ oral cancer population in Hong Kong

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