Estimation with heteroscedastic and correlated errors: A spatial analysis of intra-urban mortality data

1991 ◽  
Vol 70 (3) ◽  
pp. 223-241 ◽  
Author(s):  
Robert Haining
Plant Disease ◽  
2006 ◽  
Vol 90 (9) ◽  
pp. 1171-1180 ◽  
Author(s):  
D. M. Benson ◽  
L. F. Grand ◽  
C. S. Vernia ◽  
T. R. Gottwald

In 1999, 19 plots of Fraser fir (Abies fraseri) with a disease focus were established in commercial plantings grown for Christmas tree production in the mountains of five western North Caro-lina counties. Progress of Phytophthora root rot caused by Phytophthora cinnamomi as estimated by mortality was followed in each plot over 3 to 4 years in an attempt to understand dispersal of inoculum. Slope, aspect, and field production age at the time plots were established were recorded. Rainfall estimated from National Weather Service stations each growing season also was recorded. The relationship of site parameters and rainfall to dispersal and disease was investigated. Disease incidence and mortality were assessed in June and September each year for 3 or 4 years depending on plot. Phytophthora root rot as estimated by mortality counts over time in a logistic regression model progressed in only five of 19 plots over 3 years. None of the site parameters correlated with mortality data, although slightly more disease was found in plots with a north aspect. Rainfall was below normal in the 3 years of the study and did not correlate with mortality in any year. Lack of disease progress in the majority of plots was attributed to drought conditions in the region. In the five plots where mortality increased over time, spatial analysis suggested an aggregated pattern of diseased plants. Aggregation was apparent but not very strong among nearest neighbors, but was considerably stronger among groups of trees within a local area. This aggregation within groups was stronger when larger group sizes were examined by beta-binomial analysis. A spatial analysis by distance indices method (SADIE) indicated the presence of secondary clusters occurring several meters away from the main focus. A stochastic model also was employed that indicated a combination of spatial processes were likely involved, specifically a tendency toward spread within a local area, but not necessarily to the nearest neighboring trees, combined with an influence of background inoculum that could not be accounted for within local areas and may have come from external sources. Thus, all sources of inoculum including infected planting stock, inoculum in soil, infected trees, and contaminated equipment were equally important in epidemics of Phytophthora root rot in Fraser fir and dispersal of P. cinnamomi.


2020 ◽  
Vol 60 (1) ◽  
Author(s):  
Jesús Enrique Andrades-Grassi ◽  
Ledyz Cuesta-Herrera ◽  
Guillermo Bianchi-Pérez ◽  
Hilda Cristina Grassi ◽  
Juan Ygnacio López-Hernández ◽  
...  

Disease mapping seeks to represent the risk of a disease. This paper focuses on the spatial analysis of risk for pandemic COVID-19 in Europe and the Mediterranean. Morbidity and mortality data for 54 countries in ratio format were used. Two hypotheses were considered, the first one is that the data are homogeneous and the second one is that the ratios are defined in a heterogeneous manner requiring the stratification on the basis of covariables and the methodology of Jenks’ intervals. Spatial risk models were applied as well as methods for the representation of clusters. The results show that the best representation is obtained with the Poisson-Gamma Model under stratification. The variations in the ratios are due to the individual policies of each country for the management of the pandemic. The cluster analysis shows that there is a high mortality process in Eastern Europe. The behavior of the pandemic should be evaluated in the space-time process as well as in other heterogeneous and highly unequal regions.


2018 ◽  
Vol 13 (2) ◽  
Author(s):  
José Augusto Passos Góes ◽  
Danilo de Gois Souza ◽  
Lucas Almeida Andrade ◽  
Jéssica Cunha ◽  
Simone Kameo ◽  
...  

This is an ecological study with exploratory analysis of spatial and temporal data based on mortality data with respect to prostate cancer obtained from the Mortality Information System concerning residents of the state of Sergipe, Brazil between 2000 and 2015. The analysis of temporal trends was performed using the Joinpoint Regression Program through Poisson regression. Spatial analysis was performed using the empirical Bayesian model, Kernel analysis, Global Moran and Local indices. There were 1,986 deaths due to prostate cancer, most of which occurring after 60 years of age. An increasing, non-constant but significant trend in mortality rates was noted. The kernel density estimator showed hotspot densities of the highest rates of prostate cancer mortality in the north-eastern and central regions of the state. High-risk clusters were identified for prostate cancer mortality (I = 0.55, P<0.01). There was an increase in prostate cancer mortality rates and a heterogeneous geographic distribution of risk areas, with high-risk priority areas identified in certain regions of the state. These priority areas include the municipalities located in the Northeast (Amparo do São Francisco, Aquidabã, Canhoba, Cedro de São João and Telha), the West (Frei Paulo and Pedra Mole) and the south-western region of the state (Poço Verde and Simão Dias).


Crisis ◽  
2011 ◽  
Vol 32 (4) ◽  
pp. 178-185 ◽  
Author(s):  
Maurizio Pompili ◽  
Marco Innamorati ◽  
Monica Vichi ◽  
Maria Masocco ◽  
Nicola Vanacore ◽  
...  

Background: Suicide is a major cause of premature death in Italy and occurs at different rates in the various regions. Aims: The aim of the present study was to provide a comprehensive overview of suicide in the Italian population aged 15 years and older for the years 1980–2006. Methods: Mortality data were extracted from the Italian Mortality Database. Results: Mortality rates for suicide in Italy reached a peak in 1985 and declined thereafter. The different patterns observed by age and sex indicated that the decrease in the suicide rate in Italy was initially the result of declining rates in those aged 45+ while, from 1997 on, the decrease was attributable principally to a reduction in suicide rates among the younger age groups. It was found that socioeconomic factors underlined major differences in the suicide rate across regions. Conclusions: The present study confirmed that suicide is a multifaceted phenomenon that may be determined by an array of factors. Suicide prevention should, therefore, be targeted to identifiable high-risk sociocultural groups in each country.


Author(s):  
Desfira Ahya ◽  
Inas Salsabila ◽  
Miftahuddin

Angka Kematian Bayi/ Infant Mortality Rate (IMR) merupakan indikator penting dalam mengukur keberhasilan pengembangan kesehatan. Nilai IMR juga dapat digunakan untuk mengetahui tingkat kesehatan ibu, kondisi kesehatan lingkungan dan secara umum, tingkat pengembangan sosio-ekonomi masyarakat. Penelitian ini bertujuan untuk memperoleh model IMR terbaik menggunakan tiga pendekatan: Model Linear, Model Linear Tergeneralisir dan Model Aditif Tergeneralisir dengan basis P-spline. Sebagai tambahan, berdasarkan model tersebut akan terlihat variabel yang mempengaruhi tingkat kematian bayi di provinsi Aceh. Penelitian ini menggunakan data jumlah kematian bayi di tahun 2013-2015. Data dalam penelitian ini diperoleh dari Profil Kesehatan Aceh. Hasil menunjukkan bahwa model terbaik dalam menjelaskan angka kematian bayi di provinsi Aceh tahun 2013-2015 ialah Model Linear Tergeneralisir dengan basis P-spline menggunakan parameter penghalusan 100 dan titik knots 8. Faktor yang sangat mempengaruhi angka kematian ialah jumlah pekerja yang sehat.   Infant mortality rate (IMR) is an important indicator in measuring the success of health development. IMR also can be used to knowing the level of maternal health, environmental health conditions and generally the level of socio-economic development in community. This research aims to get the best model of infant mortality data using three approaches: Linear Model, Generalized Linear Model and Generalized Additive Model with Penalized Spline (P-spline) base. In addition, based on the model can be seen the variables that affect to infant mortality in Aceh Province. This research uses data number of infant mortality in Aceh Province period 2013-2015. The data in this research were obtained from Aceh’s Health Profile. The results show that the best model can be explain infant mortality rate in Aceh Province period 2013-2015 is GAM model with P-spline base using smoothing parameter 100 and knots 8. Factor that high effect to infant mortality is number of health workers.


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