ABSTRACT
INTRODUCTION: The objective of this study was to analyze the geographic variability and the relationship between social determinants of health and COVID-19 lethality in Bariloche. METHODS: A database from the National Epidemiological Surveillance System was used to analyze COVID-19 positive cases from January 2020 to December 2021. The data were geocoded and incorporated into a geographic information system (GIS). A three-step analytical framework was applied to measure health inequity, using socioeconomic indicators and access to services. A multivariate analysis was conducted to predict fatality. RESULTS: A total of 25 020 COVID-19 cases were diagnosed in Bariloche during the study period. The fatality rate was 2.1%. Significant variability in socioeconomic indicators was observed among different territorial delegations of the city. DISCUSSION: The results showed health inequities and an association between social determinants and COVID-19 lethality in Bariloche. Individuals living in areas with higher socioeconomic vulnerability had a higher risk of mortality. These findings highlight the importance of addressing health inequities in a pandemic response.
Introducción: El objetivo de este estudio fue examinar cómo la variabilidad geográfica y los determinantes sociales de la salud influyen en la tasa de letalidad por COVID-19 en Bariloche. Métodos: Se utilizó una base de datos del Sistema Nacional de Vigilancia Epidemiológica para analizar los casos positivos de COVID-19 desde enero de 2020 hasta diciembre de 2021. Los datos se geo-codificaron y se incorporaron en un sistema de información geográfica (SIG). Se aplicó un marco de análisis en tres pasos para medir la inequidad en salud, utilizando indicadores socioeconómicos y de acceso a servicios. Se realizó un análisis multivariado para predecir la letalidad. Resultados: Se diagnosticaron un total de 25 020 casos de COVID-19 en Bariloche durante el período de estudio. La letalidad fue del 2.1%. Se observó una variabilidad significativa en indicadores socioeconómicos entre las diferentes delegaciones territoriales de la ciudad. Discusión: Los resultados mostraron inequidades en salud y una asociación entre determinantes sociales y letalidad por COVID-19 en Bariloche. Las personas que vivían en áreas con mayor vulnerabilidad socioeconómica presentaron un mayor riesgo de mortalidad. Estos hallazgos resaltan la importancia de abordar las inequidades en salud en la respuesta a una pandemia.
Subject(s)
COVID-19 , Health Inequities , Humans , COVID-19/mortality , Multivariate Analysis , Socioeconomic Factors , Argentina/epidemiologyABSTRACT
Resumen Introducción : El objetivo de este estudio fue exami nar cómo la variabilidad geográfica y los determinantes sociales de la salud influyen en la tasa de letalidad por COVID-19 en Bariloche. Métodos : Se utilizó una base de datos del Sistema Nacional de Vigilancia Epidemiológica para analizar los casos positivos de COVID-19 desde enero de 2020 hasta diciembre de 2021. Los datos se geo-codificaron y se incorporaron en un sistema de información geográfica (SIG). Se aplicó un marco de análisis en tres pasos para medir la inequidad en salud, utilizando indicadores socioeconómicos y de acceso a servicios. Se realizó un análisis multivariado para predecir la letalidad. Resultados : Se diagnosticaron un total de 25 020 casos de COVID-19 en Bariloche durante el período de estudio. La letalidad fue del 2.1%. Se observó una variabilidad significativa en indicadores socioeconó micos entre las diferentes delegaciones territoriales de la ciudad. Discusión : Los resultados mostraron inequida des en salud y una asociación entre determinantes sociales y letalidad por COVID-19 en Bariloche. Las personas que vivían en áreas con mayor vulnerabili dad socioeconómica presentaron un mayor riesgo de mortalidad. Estos hallazgos resaltan la importancia de abordar las inequidades en salud en la respuesta a una pandemia.
Abstract Introduction : The objective of this study was to ana lyze the geographic variability and the relationship between social determinants of health and COVID-19 lethality in Bariloche. Methods : A database from the National Epidemiologi cal Surveillance System was used to analyze COVID-19 positive cases from January 2020 to December 2021. The data were geocoded and incorporated into a geo graphic information system (GIS). A three-step analytical framework was applied to measure health inequity, us ing socioeconomic indicators and access to services. A multivariate analysis was conducted to predict fatality. Results : A total of 25 020 COVID-19 cases were diag nosed in Bariloche during the study period. The fatality rate was 2.1%. Significant variability in socioeconomic indicators was observed among different territorial delegations of the city. Discussion : The results showed health inequities and an association between social determinants and COVID-19 lethality in Bariloche. Individuals living in areas with higher socioeconomic vulnerability had a higher risk of mortality. These findings highlight the importance of addressing health inequities in a pan demic response.
ABSTRACT
OBJECTIVE: To study the association between neighborhood risk and moderate to severe neurodevelopmental impairment (NDI) at 22-26 months corrected age in children born at <34 weeks of gestation. We hypothesized that infants born preterm living in high-risk neighborhoods would have a greater risk of NDI and cognitive, motor, and language delays. STUDY DESIGN: We studied a retrospective cohort of 1291 infants born preterm between 2005 and 2016, excluding infants with congenital anomalies. NDI was defined as any one of the following: a Bayley Scales of Infant and Toddler Development-III Cognitive or Motor composite score <85, bilateral blindness, bilateral hearing impairment, or moderate-severe cerebral palsy. Maternal addresses were geocoded to identify census block groups and create high-risk versus low-risk neighborhood groups. Bivariate and regression analyses were run to assess the impact of neighborhood risk on outcomes. RESULTS: Infants from high-risk (n = 538; 42%) and low-risk (n = 753; 58%) neighborhoods were compared. In bivariate analyses, the risk of NDI and cognitive, motor, and language delays was greater in high-risk neighborhoods. In adjusted regression models, the risks of NDI (OR, 1.43; 95% CI, 1.04-1.98), cognitive delay (OR, 1.62; 95% CI, 1.15-2.28), and language delay (OR, 1.58; 95% CI, 1.15-2.16) were greater in high-risk neighborhoods. Breast milk at discharge was more common in low-risk neighborhoods and was protective of NDI in regression analysis. CONCLUSIONS: High neighborhood risk provides an independent contribution to preterm adverse NDI, cognitive, and language outcomes. In addition, breast milk at discharge was protective. Knowledge of neighborhood risk may inform the targeted implementation of programs for socially disadvantaged infants.
Subject(s)
Cerebral Palsy , Language Development Disorders , Neurodevelopmental Disorders , Child , Cohort Studies , Developmental Disabilities/epidemiology , Developmental Disabilities/etiology , Female , Gestational Age , Humans , Infant , Infant, Newborn , Neurodevelopmental Disorders/epidemiology , Neurodevelopmental Disorders/etiology , Retrospective StudiesABSTRACT
To overcome the challenge of obtaining accurate data on community food retail, we developed an innovative tool to automatically capture food retail data from Google Earth (GE). The proposed method is relevant to non-commercial use or scholarly purposes. We aimed to test the validity of web sources data for the assessment of community food retail environment by comparison to ground-truth observations (gold standard). A secondary aim was to test whether validity differs by type of food outlet and socioeconomic status (SES). The study area included a sample of 300 census tracts stratified by SES in two of the largest cities in Brazil, Rio de Janeiro and Belo Horizonte. The GE web service was used to develop a tool for automatic acquisition of food retail data through the generation of a regular grid of points. To test its validity, this data was compared with the ground-truth data. Compared to the 856 outlets identified in 285 census tracts by the ground-truth method, the GE interface identified 731 outlets. In both cities, the GE interface scored moderate to excellent compared to the ground-truth data across all of the validity measures: sensitivity, specificity, positive predictive value, negative predictive value and accuracy (ranging from 66.3 to 100%). The validity did not differ by SES strata. Supermarkets, convenience stores and restaurants yielded better results than other store types. To our knowledge, this research is the first to investigate using GE as a tool to capture community food retail data. Our results suggest that the GE interface could be used to measure the community food environment. Validity was satisfactory for different SES areas and types of outlets.
Subject(s)
Food Supply , Restaurants , Brazil , Cities , Commerce , Data Mining , Humans , Residence CharacteristicsABSTRACT
In recent years, Online Social Networks (OSNs) have received a great deal of attention for their potential use in the spatial and temporal modeling of events owing to the information that can be extracted from these platforms. Within this context, one of the most latent applications is the monitoring of natural disasters. Vital information posted by OSN users can contribute to relief efforts during and after a catastrophe. Although it is possible to retrieve data from OSNs using embedded geographic information provided by GPS systems, this feature is disabled by default in most cases. An alternative solution is to geoparse specific locations using language models based on Named Entity Recognition (NER) techniques. In this work, a sensor that uses Twitter is proposed to monitor natural disasters. The approach is intended to sense data by detecting toponyms (named places written within the text) in tweets with event-related information, e.g., a collapsed building on a specific avenue or the location at which a person was last seen. The proposed approach is carried out by transforming tokenized tweets into word embeddings: a rich linguistic and contextual vector representation of textual corpora. Pre-labeled word embeddings are employed to train a Recurrent Neural Network variant, known as a Bidirectional Long Short-Term Memory (biLSTM) network, that is capable of dealing with sequential data by analyzing information in both directions of a word (past and future entries). Moreover, a Conditional Random Field (CRF) output layer, which aims to maximize the transition from one NER tag to another, is used to increase the classification accuracy. The resulting labeled words are joined to coherently form a toponym, which is geocoded and scored by a Kernel Density Estimation function. At the end of the process, the scored data are presented graphically to depict areas in which the majority of tweets reporting topics related to a natural disaster are concentrated. A case study on Mexico's 2017 Earthquake is presented, and the data extracted during and after the event are reported.
Subject(s)
Geographic Information Systems , Natural Disasters/prevention & control , Social Media , Algorithms , Humans , Internet , Machine Learning , Mexico , Natural Language Processing , Neural Networks, ComputerABSTRACT
BACKGROUND: Community-associated Clostridioides difficile infections (CA-CDIs) share many risk factors with health care-associated cases, although the role of socioeconomic factors is poorly understood. This study estimates the influence of several census tract-level measures of socioeconomic status on CA-CDI incidence rates. METHODS: CA-CDI case data from the New Mexico Emerging Infections Program were analyzed using quasi-Poisson regression modeling. Geocoded cases were assigned census tract-level socioeconomic measures to explore racial, ethnic and socioeconomic disparities in CA-CDI incidence. RESULTS: Regression modeling identified census tract-level socioeconomic measures as well as individual and medical measures that together accounted for 57% of the variance in CA-CDI rates. At the census tract level, socioeconomic factors associated with an increase in CA-CDI incidence included a high percentage of individuals lacking health insurance and a low percentage of individuals with low educational attainment. A subanalysis that included racial and ethnic designation revealed that ethnicity had no significant effect, but compared to white race, other races were significantly more likely to acquire CA-CDI. CONCLUSIONS: Although this work reveals the role of certain socioeconomic and race and ethnicity risk factors in the incidence of CA-CDI, it also underscores the complex relationships that exist between socioeconomic status and access to health care.
Subject(s)
Clostridioides difficile/isolation & purification , Clostridium Infections/epidemiology , Health Status Disparities , Socioeconomic Factors , Adolescent , Adult , Black or African American , Aged , Child , Child, Preschool , Clostridium Infections/ethnology , Clostridium Infections/microbiology , Community-Acquired Infections , Educational Status , Epidemiological Monitoring , Female , Health Services Accessibility/statistics & numerical data , Hispanic or Latino , Humans , Incidence , Infant , Infant, Newborn , Insurance, Health/statistics & numerical data , Male , Middle Aged , New Mexico/epidemiology , White PeopleABSTRACT
Abstract INTRODUCTION: The Middle Paranapanema watershed is known for the transmission of schistosomiasis, and there have been autochthonous cases since 1952. This study aimed to describe this disease in space and time and evaluate its current importance as a public health problem. METHODS: Thematic maps showing the risk areas for transmission of schistosomiasis, using scan statistics, and flow maps were created in the period 1978-2016. Incidence was calculated, and the existence of spatial dependence between autochthonous and imported cases was evaluated using Ripley's K12-function. Species of snails were identified in high-risk clusters. RESULTS: A total of 1,511 autochthonous cases were reported in eight of the 25 municipalities in the study area, of which 92.8% occurred in Ourinhos. A total of 2,189 imported cases were reported (27% in Ourinhos and 20% in Assis), mainly originating in the states of Paraná and Minas Gerais. Clusters of autochthonous and imported cases with higher risk were identified in Ourinhos, Assis and Ipaussu. However, over the years, the cases began to occur in low density in Ourinhos and no longer in other municipalities in the region. The cluster detected in the period 2007-2016 in Ourinhos still has risk for the transmission of schistosomiasis. K12-function analysis indicated positive spatial dependence between autochthonous and imported cases. CONCLUSIONS: The study showed that, currently, schistosomiasis as a public health problem in Middle Paranapanema is restricted to Ourinhos. This fact may be related to the presence of Biomphalaria glabrata at a specific point and low coverage of basic sanitation.
Subject(s)
Humans , Animals , Schistosomiasis mansoni/epidemiology , Schistosoma mansoni , Biomphalaria , Schistosomiasis mansoni/transmission , Brazil/epidemiology , Residence Characteristics , Public Health , Incidence , Rivers , Spatial AnalysisABSTRACT
Geographic Information System GIS is a technology developed to generate and to analyze spatial information on several thematic areas. This work provides a method to generate and to quantify small extension areas, like morphological homogeneous units (MHU) of a soil cultural profile, using the software Arcview8. The MHU were easily identified and their areas quantified by depth level.
O Sistema de Informação Geográfica - SIG é uma tecnologia que muito auxilia a confecção e análise de mapas do espaço físico-territorial e ambiental. Este trabalho visa propor um método para quantificar áreas de tamanho pequeno, como as unidades morfologicamente homogêneas (UMH) de um perfil cultural do solo, utilizando o programa computacional Arcview8. As UMH´s foram facilmente identificadas e suas áreas quantificadas por profundidade.