Search

Extant literature investigates the impact of COVID-19 on mental health outcomes, however there is a paucity of work examining mental health distress as a risk factor for COVID-19 outcomes. While systemic variables like income inequality relate to both mental health and COVID-19, more work is needed to test theoretically informed models including such variables. Using a social-ecological framework, we aimed to address these gaps in the literature by conducting a neighborhood-level analysis of potential mental health distress and systemic- (income inequality) level predictors of reported COVID-19 infection and mortality over time in Chicago. Neighborhood-level comparisons revealed differences in mental health distress, income inequality, and reported COVID-19 mortality, but not reported COVID-19 infection. Specifically, Westside and Southside neighborhoods generally reported higher levels of mental health distress and greater concentration of poverty. The Central neighborhood showed a decline in reported mortality rates over time. Multi-level negative binomial models established that Zip-codes with greater mental health distress were at increased reported COVID-19 infection risk, yet lower mortality risk; Zip-codes with more poverty were at increased reported COVID-19 infection risk, yet lower mortality risk; and Zip-codes with the highest percentage of People of Color were at decreased risk of reported COVID-19 mortality. Taken together, these findings substantiate Chicago neighborhood-level disparities in mental health distress, income inequality, and reported COVID-19 mortality; identify unique differential associations of mental health distress and income inequality to reported COVID-19 infection and reported mortality risk; and, offer an alternative lens towards understanding COVID-19 outcomes in terms of race/ethnicity.

Background: The objective of this study was to identify which air pollutants, atmospheric variables and health determinants could influence COVID-19 mortality in Spain. This study used information from 41 of the 52 provinces in Spain (from Feb. 1, to May 31, 2021). Generalized Linear Models (GLM) with Poisson link were carried out for the provinces, using the Rate of Mortality due to COVID-19 (CM) per 1,000,000 inhabitants as dependent variables, and average daily concentrations of PM10 and NO2 as independent variables. Meteorological variables included maximum daily temperature (Tmax) and average daily absolute humidity (HA). The GLM model controlled for trend, seasonalities and the autoregressive character of the series. Days with lags were established. The relative risk (RR) was calculated by increases of 10 g/m3 in PM10 and NO2 and by 1 ℃ in the case of Tmax and 1 g/m3 in the case of HA. Later, a linear regression was carried out that included the social determinants of health. Results: Statistically significant associations were found between PM10, NO2 and the CM. These associations had a positive value. In the case of temperature and humidity, the associations had a negative value. PM10 being the variable that showed greater association, with the CM followed of NO2 in the majority of provinces. Anyone of the health determinants considered, could explain the differential geographic behavior. Conclusions: The role of PM10 is worth highlighting, as the chemical air pollutant for which there was a greater number of provinces in which it was associated with CM. The role of the meteorological variables—temperature and HA—was much less compared to that of the air pollutants. None of the social determinants we proposed could explain the heterogeneous geographical distribution identified in this study.

Count modelling is an increasingly important area of research in applied statistics. Classic count models do not satisfy the demand for problems of dispersion. The rise of Generalized Poisson distribution and Zero Inflated series distributions innovates a proliferation of studies. However, questions have been raised about the intimate connections and relationships among the Generalized Poisson distribution and Zero Inflated series distributions. A considerable amount of literature has been published on the comparison either among the Generalized Poisson distribution, Negative Binomial distribution and Poisson distribution or among Zero Inflated series distributions from a frequentist perspective. In this thesis, we critically evaluate whether the Generalized Poisson model, Zero Inflated Poisson model, Zero Inflated Negative Binomial model are interchangeable or not in exceptional circumstances from a Bayesian viewpoint. The objective is to compare estimated predictions of the mean with three models. To implement our goal, one Metropolis-Hastings algorithm is proposed. We perform a simulation study with data having Generalized Poisson distributions and then apply the Generalized Poisson model, Zero Inflated Poisson model, Zero Inflated Negative Binomial model to the same data set. Posterior distributions of the expectation are obtained based on data set and non-informative priors. Point estimates of the expectation with the Highest Posterior Density interval are calculated and play an important role in model selection together with Cross Validation and posterior predictive checks. Finally, this thesis attempts to demonstrate that the Zero Inflated Poisson model and Zero Inflated Negative Binomial model are superior to the Generalized Poisson model when data with a Generalized Poisson distribution has massive zeros. Forasmuch the dramatic impact of COVID-19, researchers are more concerned about the confirmed cases, but inflated deaths toll also deserves some attention

Most of the existing literature concerning the links between built environment and COVID-19 outcomes is based on aggregate spatial data averaged across entire cities or counties. We present neighborhood level results linking census tract-level built environment and active/sedentary travel measures with COVID-19 hospitalization and mortality rates in King County Washington. Substantial variations in COVID-19 outcomes and built environment features existed across neighborhoods. Using rigorous simulation-assisted discrete outcome random parameter models, the results shed new lights on the direct and indirect connections between built environment, travel behavior, positivity, hospitalization, and mortality rates. More mixed land use and greater pedestrian-oriented street connectivity is correlated with lower COVID-19 hospitalization/fatality rates. Greater participation in sedentary travel correlates with higher COVID-19 hospitalization and mortality whereas the reverse is true for greater participation in active travel. COVID-19 hospitalizations strongly mediate the relationships between built environment, active travel, and COVID-19 survival. Ignoring unobserved heterogeneity even when higher resolution smaller area spatial data are harnessed leads to inaccurate conclusions.

Count modelling is an increasingly important area of research in applied statistics. Classic count models do not satisfy the demand for problems of dispersion. The rise of Generalized Poisson distribution and Zero Inflated series distributions innovates a proliferation of studies. However, questions have been raised about the intimate connections and relationships among the Generalized Poisson distribution and Zero Inflated series distributions. A considerable amount of literature has been published on the comparison either among the Generalized Poisson distribution, Negative Binomial distribution and Poisson distribution or among Zero Inflated series distributions from a frequentist perspective. In this thesis, we critically evaluate whether the Generalized Poisson model, Zero Inflated Poisson model, Zero Inflated Negative Binomial model are interchangeable or not in exceptional circumstances from a Bayesian viewpoint. The objective is to compare estimated predictions of the mean with three models. To implement our goal, one Metropolis-Hastings algorithm is proposed. We perform a simulation study with data having Generalized Poisson distributions and then apply the Generalized Poisson model, Zero Inflated Poisson model, Zero Inflated Negative Binomial model to the same data set. Posterior distributions of the expectation are obtained based on data set and non-informative priors. Point estimates of the expectation with the Highest Posterior Density interval are calculated and play an important role in model selection together with Cross Validation and posterior predictive checks. Finally, this thesis attempts to demonstrate that the Zero Inflated Poisson model and Zero Inflated Negative Binomial model are superior to the Generalized Poisson model when data with a Generalized Poisson distribution has massive zeros. Forasmuch the dramatic impact of COVID-19, researchers are more concerned about the confirmed cases, but inflated deaths toll also deserves some attention