A Comparison of Poisson Model and Modified Poisson Model in Modelling Relative Risk of Childhood Diabetes in Kenya

Abstract Background Currently syphilis is considered an epidemic disease worldwide. The objective of this study was to identify intra-urban differentials in the occurrence of congenital and acquired syphilis and syphilis in pregnant women in the city of Natal, in northeast Brazil. Methods Cases of syphilis recorded by the municipal surveillance system from 1 January 2011 to 30 December 2018 were analysed. Spatial statistical analyses were performed using the kernel density estimator of the quadratic smoothing function (weighted). SaTScan software was applied for the calculation of risk based on a discrete Poisson model. Results There were 2163 cases of acquired syphilis, 738 cases of syphilis in pregnant women and 1279 cases of congenital syphilis. Kernel density maps showed that the occurrence of cases is more prevalent in peripheral areas and in areas with more precarious urban infrastructure. In 2011–2014 and 2015–2018, seven statistically significant clusters of acquired syphilis were identified. From 2011 to 2014, the most likely cluster had a relative risk of 3.54 (log likelihood ratio [LLR] 38 895; p<0.001) and from 2015 to 2018 the relative risk was 0.54 (LLR 69 955; p<0.001). Conclusions In the municipality of Natal, there was a clustered pattern of spatial distribution of syphilis, with some areas presenting greater risk for the occurrence of new cases.

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Year-round monitoring reveals prevalence of fatal bird-window collisions at the Virginia Tech Corporate Research Center

PeerJ ◽

10.7717/peerj.4562 ◽

2018 ◽

Vol 6 ◽

pp. e4562 ◽

Cited By ~ 8

Author(s):

Rebecca M. Schneider ◽

Christine M. Barton ◽

Keith W. Zirkle ◽

Caitlin F. Greene ◽

Kara B. Newman

Keyword(s):

Relative Risk ◽

Land Cover ◽

Poisson Model ◽

Credible Interval ◽

The United States ◽

Virginia Tech ◽

Research Center ◽

Bayesian Hierarchical ◽

Significant Relationships ◽

Ornamental Trees

Collisions with glass are a serious threat to avian life and are estimated to kill hundreds of millions of birds per year in the United States. We monitored 22 buildings at the Virginia Tech Corporate Research Center (VTCRC) in Blacksburg, Virginia, for collision fatalities from October 2013 through May 2015 and explored possible effects exerted by glass area and surrounding land cover on avian mortality. We documented 240 individuals representing 55 identifiable species that died due to collisions with windows at the VTCRC. The relative risk of fatal collisions at all buildings over the study period were estimated using a Bayesian hierarchical zero-inflated Poisson model adjusting for percentage of tree and lawn cover within 50 m of buildings, as well as for glass area. We found significant relationships between fatalities and surrounding lawn area (relative risk: 0.96, 95% credible interval: 0.93, 0.98) as well as glass area on buildings (RR: 1.30, 95% CI [1.05–1.65]). The model also found a moderately significant relationship between fatal collisions and the percent land cover of ornamental trees surrounding buildings (RR = 1.02, 95% CI [1.00–1.05]). Every building surveyed had at least one recorded collision death. Our findings indicate that birds collide with VTCRC windows during the summer breeding season in addition to spring and fall migration. The Ruby-throated Hummingbird (Archilochus colubris) was the most common window collision species and accounted for 10% of deaths. Though research has identified various correlates with fatal bird-window collisions, such studies rarely culminate in mitigation. We hope our study brings attention, and ultimately action, to address this significant threat to birds at the VTCRC and elsewhere.

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Geographical Analysis of Disease in Small Areas Using Hierarchical Bayesian Models

Geographic Information Systems and Health Applications ◽

10.4018/978-1-59140-042-4.ch008 ◽

2011 ◽

pp. 139-153

Author(s):

C. L. Vidal-Rodeiro ◽

M. I. Santiago-Perez ◽

E. Vazquez-Fernandez ◽

M. E. Lopez-Vizcaino ◽

X. Hervada-Vidal

Keyword(s):

Lung Cancer ◽

Relative Risk ◽

Geographical Variation ◽

Poisson Model ◽

Lung Cancer Mortality ◽

Maximum Likelihood Estimates ◽

Classical Approach ◽

Mortality Pattern ◽

Small Areas ◽

Standardized Mortality Ratios

The purpose of this chapter is to review and compare two techniques to map the mortality risk of a disease in small geographical areas. The first one is a classical approach consisting of mapping standardized mortality ratios, which are maximum likelihood estimates of the relative risk under a Poisson model of death counts. In a second step, the authors consider a Bayesian approach that assumes a hierarchical model where the death counts follow a Poisson distribution conditioned by the prior information. These methods have been applied to the study of geographical variation in men’s lung cancer mortality from 1978 to 1998 in Galicia, Spain. Mapping mortality using the first method has important drawbacks, and there are difficulties to distinguish the mortality pattern. The Bayesian methodology produces smoother maps with a clear mortality pattern and has many advantages over the classical approach.

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Overview of statistical methods for estimating the relative risk of delayed respiratory effect related to ambient air pollution exposure

Przeglad Epidemiologiczny ◽

10.32394/pe.74.61 ◽

2020 ◽

pp. 695-706

Keyword(s):

Risk Assessment ◽

Air Pollution ◽

Relative Risk ◽

Health Effect ◽

Poisson Model ◽

Ambient Air ◽

Ambient Air Pollution ◽

Distributed Lag ◽

Air Pollution Exposure ◽

Pollution Exposure

INTRODUCTION. The occurrence of smog episodes and their significant impact on human health have forced research focused on risk assessment. Over the years, methods of exposure measuring have been improved, as well as statistical models necessary to the biological response estimation including the risk of incidence or death. AIM. The aim of presented study is to review and evaluate possibilities of statistical methods of delayed respiratory health effects risk assessment related to ambient air pollution exposure. MATERIAL AND METHODS. The review of published data was carried using the PubMed platform from 1994 to the 2020 year. Over 80 references were include in the analysis identifying general characteristics, construction of models estimating the relative risk of respiratory incidents with delayed health effect, and modelling tools available in statistical packages R, SAS, and Statistica. RESULTS. Among various methods of health risk assessment, the Almon model, the Poisson model, and the Distributed Lag Non-Linear Models (DLNM) were most common used. Initially, the Poisson model was used, close to 60% of the cited works apply this method. The interest in the nonlinear modelling implementation has increased (34% of cited papers) in recent years. Mostly researchers used R or SAS statistical software. Usually, was calculated the relative risk of health effect related to short-term exposure (up to a week). About 75% of available papers concern measurements of relative risk in response to the concentration of pollution increase by unit=10 μg/m3. Other describe the risk associated with the exposure increasing by the interquartile range (IQR). CONCLUSIONS. Distributed Lag Non-linear Model DLNM is classified as the statistical tool recommended by researchers due to its flexibility in defining, simplicity in interpretation, and increasingly frequent applications to environmental epidemiology.

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Multiple Sclerosis incidence rate in southern Iran: A Bayesian epidemiological study

10.21203/rs.3.rs-34188/v2 ◽

2020 ◽

Author(s):

Naeimehossadat Asmarian ◽

Zahra Sharafi ◽

Amin Mousavi ◽

Reis Jacques ◽

Ibon Tamayo ◽

...

Keyword(s):

Multiple Sclerosis ◽

Vitamin D ◽

Relative Risk ◽

Incidence Rate ◽

Vitamin D3 ◽

Poisson Model ◽

Incidence Rates ◽

Fars Province ◽

Spatio Temporal ◽

Low Incidence

Abstract Background: Multiple Sclerosis (MS) remains to be a public health challenge, due to its unknown biological mechanisms and clinical impacts on young people. The prevalence of this disease in Iran is reported to be 5.3 to 74.28 per 100,000-person. Because of high prevalence of this disease in Fars province, the purpose of this study was to assess the spatial pattern of MS incidence rate by modeling both the effects of spatial dependence between neighboring regions and risk factors in a Bayesian Poisson model, which can lead to the improvement of health resource allocation decisions.Method: Data from 5,468 patients diagnosed with MS were collected, according to the McDonald’s criteria. New cases of MS were reported by the MS Society of Fars province from 1991 until 2016. The effects of percentage of vitamin D intake, smoking, percentage of people with normal BMI and alcohol consumption in addition to spatial structure in a Bayesian spatio-temporal hierarchical model were used to determine the relative risk and trend of MS incidence rate in 29 counties of Fars province. Results: County-level crude incidence rates ranged from 0.22 to 11.31 cases per 100,000-person population. The highest relative risk was estimated at 1.8 in the county of Shiraz, the capital of Fars province, while the lowest relative risk was estimated at 0.11 in Zarindasht county in southern of Fars. The percentages of vitamin D3 intake and smoking were significantly associated with the incidence rate of MS. The results showed that 1% increase in Vitamin D3 intake is associated with 2% decrease in the risk of MS and 1% increase in smoking is associated with 16% increase in the risk of MS.Conclusion: Spatial analysis of MS revealed low incidence rate of this disease in the south and south east of Fars province, which is due to the effects of different covariates. As suggested by previous studies, vitamin D and smoking, among all covaiates, might be associated with high incidence of MS.

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Administrative Censoring in Ecological Analyses of Autism and a Bayesian Solution

Journal of Environmental and Public Health ◽

10.1155/2011/202783 ◽

2011 ◽

Vol 2011 ◽

pp. 1-5 ◽

Cited By ~ 6

Author(s):

Scott M. Bartell ◽

Thomas A. Lewandowski

Keyword(s):

Data Analysis ◽

Relative Risk ◽

School Districts ◽

Health Outcomes ◽

Censored Data ◽

Poisson Model ◽

Toxic Release Inventory ◽

Mercury Emissions ◽

State Educational ◽

The Bayesian Approach

Widely cited ecological analyses of autism have reported associations with mercury emissions, with precipitation, and race at the level of counties or school districts. However, state educational agencies often suppress any low numerical autism counts before releasing data—a phenomenon known as “administrative censoring.” Previous analyses did not describe appropriate methods for censored data analysis; common substitution or exclusion methods are known to introduce bias and produce artificially narrow confidence intervals. We apply a Bayesian censored random effects Poisson model to reanalyze associations between 2001 Toxic Release Inventory reported mercury emissions and 2000-2001 autism counts in Texas. Relative risk estimates for autism decreased from 4.44 (95% CI: 4.16, 4.74) per thousand lbs. of air mercury emissions using a naive zero-substitution approach to 1.42 (95% CI: 1.09, 1.78) using the Bayesian approach. Inadequate attention to censoring poses a serious threat to the validity of ecological analyses of autism and other health outcomes.

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Modeling schistosomiasis spatial risk dynamics over time in Rwanda using zero-inflated Poisson regression

Scientific Reports ◽

10.1038/s41598-020-76288-8 ◽

2020 ◽

Vol 10 (1) ◽

Author(s):

Elias Nyandwi ◽

Frank Badu Osei ◽

Tom Veldkamp ◽

Sherif Amer

Keyword(s):

Relative Risk ◽

Health Facility ◽

Spatial Regression ◽

Poisson Model ◽

Service Area ◽

Environmental Data ◽

Transmission Risk ◽

Primary Health ◽

Spatial Risk ◽

Primary Health Facility

Abstract The recorded clinical cases of S. mansoni at primary health facility level contain an excessive number of zero records. This could mean that no S. mansoni infection occurred (a true zero) in the health facility service area but it could also that at least one infection occurred but none were reported or diagnosed (a false zero). Standard statistical analysis, using exploratory or confirmatory spatial regression, fail to account for this type of data insufficiency. This study developed a zero-inflated Poisson model to explore the spatiotemporal variation in schistosomiasis risk at a fine spatial scale. We used environmental data generated at primary health facility service area level as explanatory variables affecting transmission risk. Identified risk factors were subsequently used to project the spatial variability of S. mansoni infection risk for 2050. The zero-inflated Poisson model shows a considerable increase of relative risk of the schistosomiasis over one decade. Furthermore, the changes between the risk in 2009 and forecasted risk by 2050 indicated both persistent and emerging areas with high relative risk of schistosomiasis infection. The risk of schistosomiasis transmission is 69%, 29%, and 50% higher in areas with rice cultivation, proximity to rice farms, and proximity to a water body respectively. The prediction and forecasting maps provide a valuable tool for monitoring schistosomiasis risk in Rwanda and planning future disease control initiatives in wetland ecosystem development context.

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