scholarly journals Municipality- level predictors of COVID-19 mortality in Mexico: a cautionary tale

2020 ◽  
pp. 1-23
Author(s):  
Alejandra Contreras-Manzano ◽  
Carlos M. Guerrero-López ◽  
Mercedes Aguerrebere ◽  
Ana Cristina Sedas ◽  
Héctor Lamadrid-Figueroa
Keyword(s):  
Mortality Rate ◽  
Population Density ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Obesity Prevalence ◽  
Factors Associated ◽  
Active Population ◽  
Binomial Regression ◽  
And Control

Abstract Objective Local characteristics of populations have been associated with COVID-19 outcomes. We analyze the Municipality-level factors associated with a high COVID-19 mortality rate of in Mexico. Methods We retrieved information from cumulative confirmed symptomatic cases and deaths of COVID-19 as of June 20th, 2020 and data from most recent census and surveys of Mexico. A negative binomial regression model was adjusted, dependent variable was the COVID-19 deaths and the independent variables were the quintiles of the distribution of sociodemographic and health characteristics among the 2,457 Municipalities of Mexico. Results Factors associated with high MR of COVID-19, relative to Quintile 1 were; diabetes and obesity prevalence, diabetes mortality rate, indigenous population, economically active population, density of economic units that operate essential activities and population density. Among factors inversely associated with lower MR of COVID-19 were; high hypertension prevalence and houses without drainage. We identified 1,351 municipalities without confirmed COVID-19 deaths, of which, 202 had high and 82 very high expected COVID-19 mortality (Means=8 and 13.8 deaths per 100,000 respectively). Conclusion This study identified Municipalities of Mexico that could lead to a high mortality scenario later in the epidemic and warns against premature easing of mobility restrictions and to reinforce strategies of prevention and control of outbreaks in communities vulnerable to COVID-19.

scholarly journals Municipality- level predictors of COVID-19 mortality in Mexico: a cautionary tale

2020 ◽  
Author(s):  
Alejandra Contreras-Manzano ◽  
Carlos M Guerrero-Lopez ◽  
Mercedes Aguerrebere ◽  
Ana Cristina Sedas ◽  
Hector Lamadrid-Figueroa
Keyword(s):  
Mortality Rate ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Health Determinants ◽  
Negative Binomial Regression Model ◽  
Public Official ◽  
Obesity Prevalence ◽  
Factors Associated ◽  
Active Population ◽  
Rate Ratios

Background. Inequalities and burden of comorbidities of the Coronavirus disease 2019 (COVID-19) vary importantly inside the countries. We aimed to analyze the Municipality-level factors associated with a high COVID-19 mortality rate of in Mexico. Methods. We retrieved information from 142,643 cumulative confirmed symptomatic cases and 18,886 deaths of COVID-19 as of June 20th, 2020 from the publicly available database of the Ministry of Health of Mexico. Public official data of the most recent census and surveys of the country were used to adjust a negative binomial regression model with the quintiles (Q) of the distribution of sociodemographic and health outcomes among 2,457 Municipality-level. Expected Mortality Rates (EMR), Incidence Rate Ratios (IRR) and 95% Confidence Intervals are reported. Results. Factors associated with high MR of COVID-19, relative to Quintile 1 (Q1), were; diabetes prevalence (Q4, IRR=2.60), obesity prevalence (Q5, IRR=1.93), diabetes mortality rate (Q5, IRR=1.58), proportion of indigenous population (Q2, IRR=1.68), proportion of economically active population (Q5, IRR=1.50), density of economic units that operate essential activities (Q4, IRR=1.54) and population density (Q5, IRR=2.12). We identified 1,351 Municipality-level without confirmed COVID-19 deaths, of which, 202 had nevertheless high (Q4, Mean EMR= 8.0 deaths per 100,000) and 82 very high expected COVID-19 mortality (Q5, Mean EMR= 13.8 deaths per 100,000). Conclusion. This study identified 1,351 Municipality-level of Mexico that, in spite of not having confirmed COVID-19 deaths yet, share characteristics that could eventually lead to a high mortality scenario later in the epidemic and warn against premature easing of mobility restrictions. Local information should be used to reinforce strategies of prevention and control of outbreaks in communities vulnerable to COVID-19. Keywords: COVID-19; risk factors; social determinants; health determinants; Municipality-level; counties.

scholarly journals Handling of Overdispersion in the Poisson Regression Model with Negative Binomial for the Number of New Cases of Leprosy in Java

2021 ◽  
Vol 5 (1) ◽  
pp. 1-13
Author(s):  
Yopi Ariesia Ulfa ◽  
Agus M Soleh ◽  
Bagus Sartono
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Explanatory Variables ◽  
Binomial Regression ◽  
Directorate General ◽  
The Republic ◽  
And Control

Based on data from the Directorate General of Disease Prevention and Control of the Ministry of Health of the Republic of Indonesia, in 2017, new leprosy cases that emerged on Java Island were the highest in Indonesia compared to the number of events on other islands. The purpose of this study is to compare Poisson regression to a negative binomial regression model to be applied to the data on the number of new cases of leprosy and to find out what explanatory variables have a significant effect on the number of new cases of leprosy in Java. This study's results indicate that a negative binomial regression model can overcome the Poisson regression model's overdispersion. Variables that significantly affect the number of new cases of leprosy based on the results of negative binomial regression modeling are total population, percentage of children under five years who had immunized with BCG, and percentage of the population with sustainable access to clean water.

scholarly journals Count Models for Identifying Factors Associated with Child Mortality in Ethiopia

2021 ◽  
Author(s):  
Endale Alemayehu Ali ◽  
Tsigereda Tilahun
Keyword(s):  
Mortality Rate ◽  
Regression Model ◽  
Child Mortality ◽  
Regression Models ◽  
Negative Binomial ◽  
Average Rate ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Count Models ◽  
Binomial Regression

Abstract Introduction: The under-five mortality rate, often known by its acronym U5MR or simply as the child mortality rate, indicates the probability of dying between births exactly five years of age, expressed per 1,000 live births. In comparison, the probability of dying after the first month and before reaching age 1 was 12 per 1,000, the probability of dying after age 1 and before age 5 was 10 per 1,000, and the probability of dying after age 5 and before age 15 was 7 per 1,000. Objectives: The study was aimed to determine the major factors of child mortality in Ethiopia using different counting models. In detail the study has the objective of identifying the risk factors of child mortality in Ethiopia and also to prioritize the best counting models that fit the data well. Methods: The Ethiopian demographic and health survey of 2016 was used for this study. About 10641 women aged between 15-49 were included in the survey. To analyze the data, counting models like the Poisson regression model, negative binomial model, zero-inflated regression models, and zero-inflated negative binomial regression model were applicable. Results: The results of the study indicated that of the total 10641 women respondents, 7576 (71.2%) have not faced the problem of child mortality. Thus, this result has the clue that the count models, especially the models that can handle the dispersion may be applicable. The average rate of child mortality is less than the variance of child mortality and this indicated that there is an over-dispersion of the data. Of all the candidate models, a zero-inflated negative binomial regression model was found to be the best model since it has a minimum AIC(15517). The coefficient table of the best model indicated that of child mortality for the women from rural residence is 1.2532 greater than those from urban with a 95% confidence interval (0.0905, 0.3610). Conclusion: The model comparison technique is indicated that the zero-inflated negative binomial regression models were the best mode that fit the data well. Under this model, the residency of women, birth order, Preceding Birth Interval, Size of a child at birth (smaller than average), and number of household members are significant variables in determining the status of child mortality in Ethiopia

scholarly journals Effect of Temperatures, Humidity and Population Density on the Spreading of Covid-19 at 70 Cities/Provinces

2020 ◽  
Author(s):  
Jishan Ahmed ◽  
Md. Hasnat Jaman ◽  
Goutam Saha ◽  
Pratyya Ghosh
Keyword(s):  
Population Density ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Environmental Data ◽  
Negative Binomial Regression Model ◽  
Binomial Regression ◽  
The Social ◽  
The Impact ◽  
Best Fit ◽  
Vital Factor

The main goal of this article is to demonstrate the impact of environmental data on the spreading of Covid-19. In this research, data has been collected from 70 cities/provinces that are affected by Covid-19. Here, environmental data refers to temperatures, humidity and population density in each of these cities/provinces. This data has been analyzed using statistical models such as Poisson, Quasi-Poisson and negative Binomial. It is found that a negative Binomial regression model is the best fit for our data. Our results reveal that average high temperature is the vital factor to slow down the spread of Covid-19. In addition, higher population density found to be an important factor for the quick spreading of Covid-19 where it is quite impossible to maintain the social distance and the virus can spread easily.

scholarly journals Association between fatality rate of COVID-19 and selenium deficiency in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Hai-Yang Zhang ◽  
An-Ran Zhang ◽  
Qing-Bin Lu ◽  
Xiao-Ai Zhang ◽  
Zhi-Jie Zhang ◽  
...  
Keyword(s):  
Negative Binomial ◽  
Viral Infections ◽  
Case Fatality ◽  
Selenium Deficiency ◽  
Negative Binomial Regression ◽  
Negative Binomial Regression Model ◽  
Fatality Rate ◽  
Selenium Content ◽  
Individual Level ◽  
Binomial Regression

Abstract Background COVID-19 has impacted populations around the world, with the fatality rate varying dramatically across countries. Selenium, as one of the important micronutrients implicated in viral infections, was suggested to play roles. Methods An ecological study was performed to assess the association between the COVID-19 related fatality and the selenium content both from crops and topsoil, in China. Results Totally, 14,045 COVID-19 cases were reported from 147 cities during 8 December 2019–13 December 2020 were included. Based on selenium content in crops, the case fatality rates (CFRs) gradually increased from 1.17% in non-selenium-deficient areas, to 1.28% in moderate-selenium-deficient areas, and further to 3.16% in severe-selenium-deficient areas (P = 0.002). Based on selenium content in topsoil, the CFRs gradually increased from 0.76% in non-selenium-deficient areas, to 1.70% in moderate-selenium-deficient areas, and further to 1.85% in severe-selenium-deficient areas (P < 0.001). The zero-inflated negative binomial regression model showed a significantly higher fatality risk in cities with severe-selenium-deficient selenium content in crops than non-selenium-deficient cities, with incidence rate ratio (IRR) of 3.88 (95% CIs: 1.21–12.52), which was further confirmed by regression fitting the association between CFR of COVID-19 and selenium content in topsoil, with the IRR of 2.38 (95% CIs: 1.14–4.98) for moderate-selenium-deficient cities and 3.06 (1.49–6.27) for severe-selenium-deficient cities. Conclusions Regional selenium deficiency might be related to an increased CFR of COVID-19. Future studies are needed to explore the associations between selenium status and disease outcome at individual-level.

scholarly journals Statistical models for analyzing count data: predictors of length of stay among HIV patients in Portugal using a multilevel model

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ahmed Nabil Shaaban ◽  
Bárbara Peleteiro ◽  
Maria Rosario O. Martins
Keyword(s):  
Length Of Stay ◽  
Regression Model ◽  
Random Effects ◽  
Count Data ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Comprehensive Approach ◽  
Negative Binomial Regression Model ◽  
Hiv Patients ◽  
Binomial Regression

Abstract Background This study offers a comprehensive approach to precisely analyze the complexly distributed length of stay among HIV admissions in Portugal. Objective To provide an illustration of statistical techniques for analysing count data using longitudinal predictors of length of stay among HIV hospitalizations in Portugal. Method Registered discharges in the Portuguese National Health Service (NHS) facilities Between January 2009 and December 2017, a total of 26,505 classified under Major Diagnostic Category (MDC) created for patients with HIV infection, with HIV/AIDS as a main or secondary cause of admission, were used to predict length of stay among HIV hospitalizations in Portugal. Several strategies were applied to select the best count fit model that includes the Poisson regression model, zero-inflated Poisson, the negative binomial regression model, and zero-inflated negative binomial regression model. A random hospital effects term has been incorporated into the negative binomial model to examine the dependence between observations within the same hospital. A multivariable analysis has been performed to assess the effect of covariates on length of stay. Results The median length of stay in our study was 11 days (interquartile range: 6–22). Statistical comparisons among the count models revealed that the random-effects negative binomial models provided the best fit with observed data. Admissions among males or admissions associated with TB infection, pneumocystis, cytomegalovirus, candidiasis, toxoplasmosis, or mycobacterium disease exhibit a highly significant increase in length of stay. Perfect trends were observed in which a higher number of diagnoses or procedures lead to significantly higher length of stay. The random-effects term included in our model and refers to unexplained factors specific to each hospital revealed obvious differences in quality among the hospitals included in our study. Conclusions This study provides a comprehensive approach to address unique problems associated with the prediction of length of stay among HIV patients in Portugal.

Evaluation of Strategies to Mitigate Culvert-Involved Crashes

2021 ◽  
pp. 036119812199207
Author(s):  
Hitesh Chawla ◽  
Megat-Usamah Megat-Johari ◽  
Peter T. Savolainen ◽  
Christopher M. Day
Keyword(s):  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Cost Effective ◽  
Negative Binomial Regression Model ◽  
Promising Alternative ◽  
Roadside Safety ◽  
Adverse Conditions ◽  
Traffic Characteristics ◽  
Annual Average Daily Traffic ◽  
Binomial Regression

The objectives of this study were to assess the in-service safety performance of roadside culverts and evaluate the potential impacts of installing various safety treatments to mitigate the severity of culvert-involved crashes. Such crashes were identified using standard fields on police crash report forms, as well as through a review of pertinent keywords from the narrative section of these forms. These crashes were then linked to the nearest cross-drainage culvert, which was associated with the nearest road segment. A negative binomial regression model was then estimated to discern how the risk of culvert-involved crashes varied as a function of annual average daily traffic, speed limit, number of travel lanes, and culvert size and offset. The second stage of the analysis involved the use of the Roadside Safety Analysis Program to estimate the expected crash costs associated with various design contexts. A series of scenarios were evaluated, culminating in guidance as to the most cost-effective treatments for different combinations of roadway geometric and traffic characteristics. The results of this study provide an empirical model that can be used to predict the risk of culvert-involved crashes under various scenarios. The findings also suggest that the installation of safety grates on culvert openings provides a promising alternative for most of the cases where the culvert is located within the clear zone. In general, a guardrail is recommended when adverse conditions are present or when other treatments are not feasible at a specific location.

Sign in / Sign up

Export Citation Format

Share Document