Geographically weighted bivariate zero inflated generalized Poisson regression model and its application

Purhadi, Purhadi; Sari, Dewi Novita; Aini, Qurotul; Irhamah,

doi:10.1016/j.heliyon.2021.e07491

Cited by 9 publications

(6 citation statements)

References 13 publications

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“…Next, as the data showed high spatial autocorrelation of the residuals, GWPR was applied. The GWPR is capable of creating an equation for each municipality and using it with Poisson distribution in order to smooth the inflated number of zero moderate and severe events [ 31 , 32 ]. The regression model that achieved the best performance as defined by the highest adjusted R 2 value, and the lowest value of AIC and Moran’s I residuals was used to make the study analogies [ 29 ].…”

Section: Methodsmentioning

confidence: 99%

“…Additionally, the heterogeneity of worse SBE outcomes in Brazil could PRODOC/FAPEAM, and POSGRAD) and by the Ministry of Health, Brazil (proposal No. 733781/ [19][20][21][22][23][24][25][26][27][28][29][30][31][32][33][34][35]. CJG receives research funding and research reported in this publication was partly supported by the Fogarty International Center of the National Institutes of Health under Award Number R21TW011944.…”

Section: Introductionmentioning

confidence: 99%

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Antivenom access impacts severity of Brazilian snakebite envenoming: A geographic information system analysis

Isaacson

Silva

et al. 2023

PLoS Negl Trop Dis

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Background Snakebite envenoming (SBE) is a neglected tropical disease capable of causing both significant disability and death. The burden of SBE is especially high in low- and middle-income countries. The aim of this study was to perform a geospatial analysis evaluating the association of sociodemographics and access to care indicators on moderate and severe cases of SBE in Brazil. Methods We conducted an ecological, cross-sectional study of SBE in Brazil from 2014 to 2019 using the open access National System Identification of Notifiable Diseases (SINAN) database. We then collected a set of indicators from the Brazil Census of 2010 and performed a Principal Component Analysis to create variables related to health, economics, occupation, education, infrastructure, and access to care. Next, a descriptive and exploratory spatial analysis was conducted to evaluate the geospatial association of moderate and severe events. These variables related to events were evaluated using Geographically Weighted Poisson Regression. T-values were plotted in choropleth maps and considered statistically significant when values were <-1.96 or >+1.96. Results We found that the North region had the highest number of SBE cases by population (47.83/100,000), death rates (0.18/100,000), moderate and severe rates (22.96/100,000), and proportion of cases that took more than three hours to reach healthcare assistance (44.11%). The Northeast and Midwest had the next poorest indicators. Life expectancy, young population structure, inequality, electricity, occupation, and more than three hours to reach healthcare were positively associated with greater cases of moderate and severe events, while income, illiteracy, sanitation, and access to care were negatively associated. The remaining indicators showed a positive association in some areas of the country and a negative association in other areas. Conclusion Regional disparities in SBE incidence and rates of poor outcomes exist in Brazil, with the North region disproportionately affected. Multiple indicators were associated with rates of moderate and severe events, such as sociodemographic and health care indicators. Any approach to improving snakebite care must work to ensure the timeliness of antivenom administration.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Antivenom access impacts severity of Brazilian snakebite envenoming: A geographic information system analysis

Isaacson

Silva

et al. 2023

PLoS Negl Trop Dis

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“…The GWPR model parameter estimation was carried out using the Maximum Likelihood Estimation (MLE) method, namely by maximizing the likelihood function [15]. The MLE method is usually used if the distribution of the data being modeled is known.…”

Section: Geographically Weighted Poisson Regression (Gwpr)mentioning

confidence: 99%

Rainfall Modeling Using the Geographically Weighted Poisson Regression Method

Iriany,

Ngabu,

Ariyanto

2024

BAREKENG: J. Math. & App.

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Rainfall is an important parameter in understanding the climate and environment in the Malang Regency area. This research aims to model the distribution of rainfall in this region using the Geographically Weighted Poisson Regression (GWPR) method. GWPR is a spatial statistical approach that allows us to understand changes in inhomogeneous rainfall patterns throughout the Malang Regency area. Rainfall data collected from weather stations over several years was used in this study. We use GWR to study the relationship between various environmental factors, such as topography, vegetation, and land use, and rainfall distribution in Malang Regency. The results of the GWR analysis provide a deeper understanding of the spatial differences in the influence of these factors on rainfall. By applying GWR, we can find out how certain factors contribute to different rainfall patterns in certain regions. Rainfall modeling using the Geographically Weighted Poisson Regression (GWPR) method combines the power of Poisson regression in analyzing calculated data with the advantages of GWR in modeling spatial variability. GWPR allows us to identify and map rainfall distribution patterns that vary in geographic space. The main advantage of GWPR is its ability to provide local adjustments and capture the spatial variability associated with rainfall distribution. The results of the modeling analysis show that the GWPR is better, marked by the smallest AIC value, namely 336.84, compared to the generalized poisson regression model, namely 337.76.

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“…The results of hypothesis testing indicate that the K1 -K4 visits has a significant influence on the maternal mortality. To prevent maternal mortality, the government must pay attention to the procedures for antenatal care until delivery (Purhadi et al, 2021). In Indonesia, the k1-k4 examination is highly recommended by the government.…”

Section: Relation Between K1 -K4 Visits and Maternal Mortalitymentioning

confidence: 99%

The Relationship between Age, Parity, Early Detection, K1-K4 Visits, Integrated ANC, Three Times Obgyn’s Ultrasound Examination, and Maternal Mortality

2022

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Maternal mortality is one of the indicators of health-care quality and effectiveness. Maternal mortality increases and decreases due to several factors. This article aims to analyze the factors that cause the increase and decrease in the maternal mortality rate. The research method is a cohort prospective, using a systematic sampling technique with the principle of proportionality. The number of samples is 150 data taken from the Tapin District Health Office, South Kalimantan, Indonesia. Statistical data analysis was performed using statistical descriptions and statistical inferential. The results of hypothesis testing indicate that age, early detection, K1 – K4 visits, integrated ANC and 3x SpOG ultrasound have a significant effect on maternal mortality. The parity does not have a significant effect on maternal mortality. Therefore, the pregnancy program must pay attention to age. During pregnancy, pregnant women must go through the process of early detection, K1 – K4 visits, integrated ANC.Parity must be considered so that babies can be born with conditions without deficiencies. Keywords: Age; Parity; Early Detection; K1 – K4 Visits; ANC, Ultrasound; Maternal Mortality.

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Geographically weighted bivariate zero inflated generalized Poisson regression model and its application

Cited by 9 publications

References 13 publications

Antivenom access impacts severity of Brazilian snakebite envenoming: A geographic information system analysis

Antivenom access impacts severity of Brazilian snakebite envenoming: A geographic information system analysis

Rainfall Modeling Using the Geographically Weighted Poisson Regression Method

The Relationship between Age, Parity, Early Detection, K1-K4 Visits, Integrated ANC, Three Times Obgyn’s Ultrasound Examination, and Maternal Mortality

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