Spatial analysis of COVID-19 incidence and the sociodemographic context in Brazil

Background Identified in December 2019 in the city of Wuhan, China, the outbreak of COVID-19 spread throughout the world and its impacts affect different populations differently, where countries with high levels of social and economic inequality such as Brazil gain prominence, for understanding of the vulnerability factors associated with the disease. Given this scenario, in the absence of a vaccine or safe and effective antiviral treatment for COVID-19, nonpharmacological measures are essential for prevention and control of the disease. However, many of these measures are not feasible for millions of individuals who live in territories with increased social vulnerability. The study aims to analyze the spatial distribution of COVID-19 incidence in Brazil’s municipalities (counties) and investigate its association with sociodemographic determinants to better understand the social context and the epidemic’s spread in the country. Methods This is an analytical ecological study using data from various sources. The study period was February 25 to September 26, 2020. Data analysis used global regression models: ordinary least squares (OLS), spatial autoregressive model (SAR), and conditional autoregressive model (CAR) and the local regression model called multiscale geographically weighted regression (MGWR). Findings The higher the GINI index, the higher the incidence of the disease at the municipal level. Likewise, the higher the nurse ratio per 1,000 inhabitants in the municipalities, the higher the COVID-19 incidence. Meanwhile, the proportional mortality ratio was inversely associated with incidence of the disease. Discussion Social inequality increased the risk of COVID-19 in the municipalities. Better social development of the municipalities was associated with lower risk of the disease. Greater access to health services improved the diagnosis and notification of the disease and was associated with more cases in the municipalities. Despite universal susceptibility to COVID-19, populations with increased social vulnerability were more exposed to risk of the illness.

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Crise Económica em Portugal: Evolução da Incidência de Depressão e Correlação com o Desemprego

Acta Médica Portuguesa ◽

10.20344/amp.13574 ◽

2020 ◽

Vol 33 (13) ◽

Author(s):

Inês Laplanche Coelho ◽

Mafalda Sousa-Uva ◽

Nuno Pina ◽

Sara Marques ◽

Carlos Matias-Dias ◽

...

Keyword(s):

Social Vulnerability ◽

Ecological Study ◽

Preventive Measures ◽

Portuguese Population ◽

Sentinel Network ◽

Unemployment Rates ◽

Positive Correlation ◽

National Statistics ◽

Using Data ◽

The Impact

Introduction: Previous studies have found an increase in the incidence rate of depression between 2007 – 2013 in Portugal, with a positive correlation with the unemployment rate, namely, in men. So, it was hypothesized that this increase is related with the situation of economic crisis. This study aimed to investigate if the correlation between unemployment rates and the incidence of depression is maintained in the post-crisis period of economic recovery in Portugal (2016 – 2018).Material and Methods: An ecological study was carried out, using data from the General Practitioners Sentinel Network concerning depression incidence (first episodes and relapses) and data from the National Statistics Institute on unemployment rates in the Portuguese population. The correlation coefficient was estimated using linear regression and the results were disaggregated by sex.Results: Between 2016 and 2018, there was a consistent decrease in the incidence of depression in both sexes. During the 1995 – 2018 period, a positive correlation was observed between unemployment and depression, with a coefficient of 0.833 (p = 0.005) in males and of 0.742 (p = 0.022) in females.Discussion: The reduction in the incidence of depression in both sexes observed between 2016 – 2018 corroborates a positive correlation between unemployment and depression in the Portuguese population, previously observed between 2007 – 2013.Conclusion: This study highlights the need to monitor the occurrence of mental illness in the Portuguese population, especially in moments of greatest social vulnerability in order to establish preventive measures, as a way to mitigate the impact of future economic crises.

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Variable Selection for the Spatial Autoregressive Model with Autoregressive Disturbances

Mathematics ◽

10.3390/math9121448 ◽

2021 ◽

Vol 9 (12) ◽

pp. 1448

Author(s):

Xuan Liu ◽

Jianbao Chen

Keyword(s):

Variable Selection ◽

Autoregressive Model ◽

Rapid Development ◽

Heterogeneous Data ◽

Spatial Effect ◽

Spatial Effects ◽

Spatial Autoregressive Model ◽

Monte Carlo Studies ◽

Spatial Autoregressive ◽

Theoretical Results

Along with the rapid development of the geographic information system, high-dimensional spatial heterogeneous data has emerged bringing theoretical and computational challenges to statistical modeling and analysis. As a result, effective dimensionality reduction and spatial effect recognition has become very important. This paper focuses on variable selection in the spatial autoregressive model with autoregressive disturbances (SARAR) which contains a more comprehensive spatial effect. The variable selection procedure is presented by using the so-called penalized quasi-likelihood approach. Under suitable regular conditions, we obtain the rate of convergence and the asymptotic normality of the estimators. The theoretical results ensure that the proposed method can effectively identify spatial effects of dependent variables, find spatial heterogeneity in error terms, reduce the dimension, and estimate unknown parameters simultaneously. Based on step-by-step transformation, a feasible iterative algorithm is developed to realize spatial effect identification, variable selection, and parameter estimation. In the setting of finite samples, Monte Carlo studies and real data analysis demonstrate that the proposed penalized method performs well and is consistent with the theoretical results.

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Trends in mortality from ill-defined causes among the elderly in Brazil, 1979-2013: ecological study

Sao Paulo Medical Journal ◽

10.1590/1516-3180.2016.0070010616 ◽

2016 ◽

Vol 134 (5) ◽

pp. 437-445 ◽

Cited By ~ 3

Author(s):

Davi Félix Martins Junior ◽

Ridalva Dias Martins Felzemburg ◽

Acácia Batista Dias ◽

Tania Maria Costa ◽

Pedro Nascimento Prates Santos

Keyword(s):

Ecological Study ◽

Temporal Trend ◽

Health Indicators ◽

The Elderly ◽

Simple Linear Regression ◽

Geographical Regions ◽

Proportional Mortality ◽

Males And Females ◽

Using Data ◽

Health Situation

ABSTRACT CONTEXT AND OBJECTIVE: Mortality measurements are traditionally used as health indicators and are useful in describing a population's health situation through reporting injuries that lead to death. The aim here was to analyze the temporal trend of proportional mortality from ill-defined causes (IDCs) among the elderly in Brazil from 1979 to 2013. DESIGN AND SETTING: Ecological study using data from the Mortality Information System of the Brazilian Ministry of Health. METHODS: The proportional mortality from IDCs among the elderly was calculated for each year of the study series (1979 to 2013) in Brazil, and the data were disaggregated according to sex and to the five geographical regions and states. To analyze time trends, simple linear regression coefficients were calculated. RESULTS: During the study period, there were 2,646,194 deaths from IDCs among the elderly, with a decreasing trend (ß -0.545; confidence interval, CI: -0.616 to -0.475; P < 0.000) for both males and females. This reduction was also observed in the macroregions and states, except for Amapá. The states in the northeastern region reported an average reduction of 80%. CONCLUSIONS: Mortality from IDCs among the elderly has decreased continuously since 1985, but at different rates among the different regions and states. Actions aimed at improving data records on death certificates need to be strengthened in order to continue the trend observed.

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Measuring when Uber behaves as a substitute or complement to transit: An examination of travel-time differences in Toronto

10.31219/osf.io/hvbma ◽

2019 ◽

Author(s):

Mischa Young ◽

Jeff Allen ◽

Steven Farber

Keyword(s):

Travel Time ◽

Public Transportation ◽

Ordinary Least Squares ◽

Gap Filling ◽

Service Levels ◽

Transit Service ◽

Logistic Regressions ◽

Transit Agencies ◽

Using Data ◽

The City

Policymakers in cities worldwide are trying to determine how ride-hailing services affect the ridership of traditional forms of public transportation. The level of convenience and comfort that these services provide is bound to take riders away from transit, but by operating in areas, or at times, when transit is less frequent, they may also be filling a gap left vacant by transit operations. These contradictory effects reveal why we should not merely categorize all ride-hailing services as a substitute or supplement to transit, and demonstrate the need to examine ride-hailing trips individually. Using data from the 2016 Transportation Tomorrow Survey in Toronto, we investigate the differences in travel-times between observed ride-hailing trips and their fastest transit alternatives. Ordinary least squares and ordered logistic regressions are used to uncover the characteristics that influence travel-time differences. We find that ride-hailing trips contained within the City of Toronto, pursued during peak hours, or for shopping purposes, are more likely to have transit alternatives of similar duration. Also, we find differences in travel-time often to be caused by transfers and lengthy walk- and wait-times for transit. Our results further indicate that 31% of ride-hailing trips in our sample have transit alternatives of similar duration (≤ 15 minute difference). These are particularly damaging for transit agencies as they compete directly with services that fall within reasonable expectations of transit service levels. We also find that 27% of ride-hailing trips would take at least 30 minutes longer by transit, evidence for significant gap-filling opportunity of ride-hailing services. In light of these findings, we discuss recommendations for ride-hailing taxation structures.

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