Spatial distribution of deaths due to Alzheimer's disease in the state of São Paulo, Brazil

Almeida, Milena Cristina da Silva; Gomes, Camila de Moraes Santos; Nascimento, Luiz Fernando Costa

doi:10.1590/1516-3180.2014.1324610

Cited by 14 publications

(13 citation statements)

References 16 publications

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“…hotspots for missing incidents). Spatial autocorrelation analyses have so far only been used to establish the frequency and mortality of dementia across regions [28,29], but to our knowledge, this is the first study to use this geospatial technique for dementia-related missing incidents. Importantly, we found no hotspots in the analysed region, indicating that the distribution of missing incidents is similar.…”

Section: Discussionmentioning

confidence: 99%

Geospatial Analysis of Environmental Risk Factors for Missing Dementia Patients

Puthusseryppady

Coughlan

Patel

et al. 2019

JAD

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BACKGROUND: Dementia-related missing incidents are highly prevalent but still poorly understood. This is particularly true for environmental/geospatial risk factors, which might contribute to these missing incidents. OBJECTIVE: The study aimed to conduct a retrospective, observational analysis on a large sample of missing dementia patient case records provided by the police (n = 210), covering dates from January 2014 to December 2017. In particular, we wanted to explore i) hotspot regions of missing incidents and ii) the relationship between outdoor landmark density and missing incidents. METHODS: Global spatial autocorrelation (Moran's I) was used to identify the potential hotspot regions for missing incidents. Meanwhile, spatial buffer and regression modelling were used to determine the relationship between landmark density and missing incidents. RESULTS: Our demographics measures replicated and extended previous studies of dementia-related missing incidents. Meanwhile, no hotspot regions for missing incidents were identified, whilst higher outdoor landmark density lead to increased missing incidents. CONCLUSION: Our results highlight that missing incidents do not occur in isolated hotspots of regions but instead are endemic in patients regardless of location. Higher landmark density emerges as a significant geospatial factor for missing incidents in dementia, which crucially informs future safeguarding/intervention studies.

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Section: Discussionmentioning

confidence: 99%

Geospatial Analysis of Environmental Risk Factors for Missing Dementia Patients

Puthusseryppady

Coughlan

Patel

et al. 2019

JAD

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“…Bivariate kernel density estimation has been previously applied in small-scale studies for HIV [30, 31], cancer [32, 33], Alzheimer [34] and crime intensity [35] and thus seems useful for a small-scale analysis of T2DM as well.…”

Section: Methodsmentioning

confidence: 99%

Do the risk factors for type 2 diabetes mellitus vary by location? A spatial analysis of health insurance claims in Northeastern Germany using kernel density estimation and geographically weighted regression

Kauhl¹,

Schweikart

Krafft

et al. 2016

Int J Health Geogr

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BackgroundThe provision of general practitioners (GPs) in Germany still relies mainly on the ratio of inhabitants to GPs at relatively large scales and barely accounts for an increased prevalence of chronic diseases among the elderly and socially underprivileged populations. Type 2 Diabetes Mellitus (T2DM) is one of the major cost-intensive diseases with high rates of potentially preventable complications. Provision of healthcare and access to preventive measures is necessary to reduce the burden of T2DM. However, current studies on the spatial variation of T2DM in Germany are mostly based on survey data, which do not only underestimate the true prevalence of T2DM, but are also only available on large spatial scales. The aim of this study is therefore to analyse the spatial distribution of T2DM at fine geographic scales and to assess location-specific risk factors based on data of the AOK health insurance.MethodsTo display the spatial heterogeneity of T2DM, a bivariate, adaptive kernel density estimation (KDE) was applied. The spatial scan statistic (SaTScan) was used to detect areas of high risk. Global and local spatial regression models were then constructed to analyze socio-demographic risk factors of T2DM.ResultsT2DM is especially concentrated in rural areas surrounding Berlin. The risk factors for T2DM consist of proportions of 65–79 year olds, 80 + year olds, unemployment rate among the 55–65 year olds, proportion of employees covered by mandatory social security insurance, mean income tax, and proportion of non-married couples. However, the strength of the association between T2DM and the examined socio-demographic variables displayed strong regional variations.ConclusionThe prevalence of T2DM varies at the very local level. Analyzing point data on T2DM of northeastern Germany’s largest health insurance provider thus allows very detailed, location-specific knowledge about increased medical needs. Risk factors associated with T2DM depend largely on the place of residence of the respective person. Future allocation of GPs and current prevention strategies should therefore reflect the location-specific higher healthcare demand among the elderly and socially underprivileged populations.

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“…This can serve as backing for implementation of prevention and control measures, as presented in several recent articles published in Brazil. 6,[8][9][10][11] …”

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confidence: 99%

Accidents involving motorcycles and potential years of life lost. An ecological and exploratory study

2018

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CONTEXT AND OBJECTIVE: Traffic accidents have gained prominence as one of the modern epidemics that plague the world. The objective of this study was to identify the spatial distribution of potential years of life lost (PYLL) due to accidents involving motorcycles in the state of São Paulo, Brazil. DESIGN AND SETTING: Ecological and exploratory study conducted in São Paulo. METHODS: Data on deaths among individuals aged 20-39 years due to motorcycle accidents (V20-V29 in the International Classification of Diseases, 10 th revision) in the state of São Paulo in the years 2007-2011 were obtained from DATASUS. These data were stratified into a database for the 63 microregions of this state, according to where the motorcyclist lived. PYLL rates per 100,000 inhabitants were calculated. Spatial autocorrelations were estimated using the Global Moran index (IM). Thematic, Moran and Kernel maps were constructed using PYLL rates for the age groups of 20-29 and 30-39 years. The Terraview 4.2.2 software was used for the analysis. RESULTS: The PYLL rates were 486.9 for the ages of 20-29 years and 199.5 for 30-39 years. Seventeen microregions with high PYLL rates for the age group of 20-29 years were identified. There was higher density of these rates on the Kernel map of the southeastern region (covering the metropolitan region of São Paulo). There were no spatial autocorrelations between rates. CONCLUSIONS: The data presented in this study identified microregions with high accident rates involving motorcycles and microregions that deserve special attention from regional managers and traffic experts.

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Spatial distribution of deaths due to Alzheimer's disease in the state of São Paulo, Brazil

Cited by 14 publications

References 16 publications

Geospatial Analysis of Environmental Risk Factors for Missing Dementia Patients

Geospatial Analysis of Environmental Risk Factors for Missing Dementia Patients

Do the risk factors for type 2 diabetes mellitus vary by location? A spatial analysis of health insurance claims in Northeastern Germany using kernel density estimation and geographically weighted regression

Accidents involving motorcycles and potential years of life lost. An ecological and exploratory study

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