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COVID-19 has spread worldwide with a high variability in cases and mortality between populations. This research aims to assess socioeconomic inequities of COVID-19 in the city of Cali, Colombia, during the first and second peaks of the pandemic in this city. An ecological study by neighborhoods was carried out, were COVID-19 cases were analyzed using a Bayesian hierarchical spatial model that includes potential risk factors such as the index of unsatisfied basic needs and socioeconomic variables as well as random effects to account for residual variation. Maps showing the geographic patterns of the estimated relative risks as well as exceedance probabilities were created. The results indicate that in the first wave, the neighborhoods with the greatest unsatisfied basic needs and low socioeconomic strata, were more likely to report positive cases for COVID-19. For the second wave, the disease begins to spread through different neighborhoods of the city and middle socioeconomic strata presents the highest risk followed by the lower strata. These findings indicate the importance of measuring social determinants in the study of the distribution of cases due to COVID-19 for its inclusion in the interventions and measures implemented to contain contagions and reduce impacts on the most vulnerable populations.

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BACKGROUND: Nigeria is among the top five countries in the world with the highest under-five mortality rates. In addition to the general leading causes of under-five mortality, studies have shown that disparity in sociocultural values and practices across ethnic groups in Nigeria influence child survival, thus there is a need for scientific validation. This study quantified the survival probabilities and the impact of socioeconomic and demographic factors, proximate and biological determinants, and environmental factors on the risk of under-five mortality in Nigeria. METHODS: The Kaplan-Meier survival curve, Nelson Aalen hazard curve, and components survival probabilities were estimated. The Exponential, Gamma, Log-normal, Weibull, and Cox hazard models in a Bayesian mixed effect hierarchical hazard modeling framework with spatial components were considered, and the Deviance and Watanabe Akaike information criteria were used to select the best model for inference. A [Formula: see text] level of significance was assumed throughout this work. The 2018 Nigeria Demographic and Health Survey dataset was used, and the outcome variable was the time between birth and death or birth and the date of interview for children who were alive on the day of the interview. RESULTS: Findings show that the probability of a child dying within the first two months is 0.04, and the probability of a boy child dying before attaining age five is 0.106, while a girl child is 0.094 probability. Gender, maternal education, household wealth status, source of water and toilet facility, residence, mass media, frequency of antenatal and postnatal visits, marital status, place of delivery, multiple births, who decide healthcare use, use of bednet are significant risk factors of child mortality in Nigeria. The mortality risk is high among the maternal age group below 24 and above 44years, and birth weight below 2.5Kg and above 4.5Kg. The under-five mortality risk is severe in Kebbi, Kaduna, Jigawa, Adamawa, Gombe, Kano, Kogi, Nasarawa, Plateau, and Sokoto states in Nigeria. CONCLUSION: This study accentuates the need for special attention for the first two months after childbirth as it is the age group with the highest expected mortality. A practicable way to minimize death in the early life of children is to improve maternal healthcare service, promote maternal education, encourage delivery in healthcare facilities, positive parental attitude to support multiple births, poverty alleviation programs for the less privileged, and a prioritized intervention to Northern Nigeria.

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Social networks are often measured as conduits of infection. Our prior cross-sectional analyses found that denser social ties among individuals reduces transmission of acute gastrointestinal illness (AGI) in coastal Ecuador; social networks can describe both risk and protection. We extend findings to examine how social connectedness influences AGI longitudinally in Ecuador from 2007 to 2013, a time of rapid development, using a two-stage Bayesian hierarchical model to estimate multiple network effects. A larger community network of people to discuss important matters with was consistently protective against AGI over time, and a network defined by people passing time together became a stronger measure of risk, due to increasing population density and travel. These networks were interdependent: the joint effect of having a small passing time network and large important matters network reduced the odds of AGI over time (2007: OR 1.16 (95% CI: 0.94, 1.44), 2013: OR 0.56 (95% CI: 0.45, 0.71)); and synergistic: the people an individual passed time with became the people they discussed important matters with. Focus groups emphasized that with greater remoteness came greater community cohesion resulting in safer WASH practices. Social networks can enhance and reduce health differently as social infrastructure evolves, highlighting the importance of community-level factors in a period of rapid development.

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Patient reported outcomes are gaining more attention in patient-centered health outcomes research and quality of life studies as important indicators of clinical outcomes, especially for patients with chronic diseases. Factor analysis is ideal for measuring patient reported outcomes. If there is heterogeneity in the patient population and when sample size is small, differential item functioning and convergence issues are challenges for applying factor models. Bayesian hierarchical factor analysis can assess health disparity by assessing for differential item functioning, while avoiding convergence problems. We conducted a simulation study and used an empirical example with American Indian minorities to show that fitting a Bayesian hierarchical factor model is an optimal solution regardless of heterogeneity of population and sample size.

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Ecologists studying emerging wildlife diseases need to confront the realism of imperfect pathogen detection across heterogeneous habitats to aid in conservation decisions. For example, spatial risk assessments of amphibian disease caused by Batrachochytrium dendrobatidis (Bd) has largely ignored imperfect pathogen detection across sampling sites. Because changes in pathogenicity and host susceptibility could trigger recurrent population declines, it is imperative to understand how pathogen prevalence and occupancy vary across environmental gradients. Here, we assessed how Bd occurrence, prevalence, and infection intensity in a diverse Neotropical landscape vary across streams in relation to abiotic and biotic predictors using a hierarchical Bayesian model that accounts for imperfect Bd detection caused by qPCR error. Our model indicated that the number of streams harboring Bd-infected frogs is higher than observed, with Bd likely being present at ~ 43% more streams than it was detected. We found that terrestrial-breeders captured along streams had higher Bd prevalence, but lower infection intensity, than aquatic-breeding species. We found a positive relationship between Bd occupancy probability and stream density, and a negative relationship between Bd occupancy probability and amphibian local richness. Forest cover was a weak predictor of Bd occurrence and infection intensity. Finally, we provide estimates for the minimum number of amphibian captures needed to determine the presence of Bd at a given site where Bd occurs, thus, providing guidence for cost-effective disease risk monitoring programs.

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One difficulty for real-time tracking of epidemics is related to reporting delay. The reporting delay may be due to laboratory confirmation, logistical problems, infrastructure difficulties, and so on. The ability to correct the available information as quickly as possible is crucial, in terms of decision making such as issuing warnings to the public and local authorities. A Bayesian hierarchical modelling approach is proposed as a flexible way of correcting the reporting delays and to quantify the associated uncertainty. Implementation of the model is fast due to the use of the integrated nested Laplace approximation. The approach is illustrated on dengue fever incidence data in Rio de Janeiro, and severe acute respiratory infection data in the state of Paraná, Brazil.

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El dengue es uno de los mayores problemas de salud pública en el estado Aragua. La situación se ha deteriorado en los últimos años, reportándose la mayor epidemia durante el año 2001. En los años 2002 y 2003 las tasas de exposición y riesgos relativos en municipios que conforman al estado Aragua, muestran que el área metropolitana de Maracay concentra riesgos importantes. Los municipios Girardot (capital), Francisco Linares Alcántara y Santiago Mariño, son los que concentraron los mayores riesgos. Durante ese período el número de nuevos casos de dengue aumentó especialmente durante la época de lluvias, evidenciándose la existencia de un patrón estacional. Este trabajo propone Modelos Bayesianos Jerárquicos con estructura espacio temporal que incluye variables climáticas y socio-demográficas con las cuales se identificaron factores de mayor influencia en la incidencia del dengue y se determinaron las parroquias con mayores riesgos.Los ajustes de los modelos resultantes se obtuvieron mediante técnicas con cadenas Markov Monte Carlo (MCMC) y se compararon con el criterio de información de deviancia (DIC). Estos modelos constituyen una herramienta importante que expertos en epidemiología y miembros del sector de salud pública deben considerar para el control del vector Aedes aegypti Linnaeus en el estado Aragua.

Dengue fever is a major public health problem in Aragua State, Venezuela. The situation has worsened in recent years, with a major epidemic during 2001. During 2002 and 2003 the exposition rates and relative risks of the municipalities that encompass Aragua State showed the highest relative risk of infection in the metropolitan area of Maracay. The municipalities of Girardot (capital), Francisco Linares Alcántara and Santiago Mariño concentrated the highest risk. During 2002 and 2003 the number of new dengue cases increased especially during the rainy season, showing the existence of a seasonal pattern. The present work presents Bayesian Hierarchical Models with spatio-temporal structure that included climatic and socioeconomic explanatory variables used to identify factors of major influence on dengue incidence and determined the municipalities with higher risks. Models were fitted using Markov Chain Monte Carlo (MCMC) methods and selected using the deviance information criteria (DIC), respectively. These models constitute an important tool that epidemiologists and public health officers in Aragua State have to consider for the control of the vector Aedes aegypti Linnaeus.

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