Background: Ballad Health participates in the Centers for Medicare and Medicaid Services’ (CMS) Accountable Health Communities (AHC) model. The AHC model is evaluating if universal screening, referral, and navigation services for health-related social needs (HRSN) can improve outcomes and reduce unnecessary utilization and costs of health care services. To ensure the evaluation of the model has sufficient statistical power, navigation services are expected to be provided to a minimum number of individuals. The purpose of this study is to analyze the characteristics of Ballad Health’s AHC navigation services that could be modified to improve opt-in rates. Methods: The primary outcome measure was identified as whether a beneficiary contacted via telephone opted-in or –out of the navigation program. Andersen’s Behavioral Model for Health Service Use was used as the conceptual framework for selecting covariates of interest. Enabling factors were of primary interest because alternate interventions may be designed around them. Data was pulled for the time period of November 17, 2018 through February 14, 2019. Where possible, covariates were associated with data from CMS’ AHC Data Template v3.1 to accommodate replication for all AHC bridge organizations, though additional internally-collected data, which may not be available for all bridge organizations, were needed for some variables. Chi-squared tests were performed for each covariate. Results: No statistical differences were found for the primary covariates of interest. Opt-in rates by Navigator were lowest for Navigator 5 and highest for Navigator 4 (67.53% and 88.24%). Opt-in rates by weekday of decision were lowest on Thursdays and highest on Wednesdays (64.91% and 77.42%). Opt-in rates based on time of day were lowest between 8:00am and 9:59am, and highest between 12:00pm and 1:59pm (62.50% and 100%). Opt-in rates were lowest when the decision was made six days after the screening and highest when made the same day (53.57% and 83.33%). Opt-in rates were lowest when there were five weekdays between screening and navigation decision, and highest when there were three weekdays between the screening and decision (60% and 90%). Other non-process covariates of interest that were statistically significant for opt-in rates were the presence of either food, safety, or utility needs. Conclusions: Several groups had higher opt-in rates that were not statistically significant; small sample sizes may have impacted the significance of these differences. For example, opt-in rates were higher when made the same day as the screening than when made one day after (83.33% and 74.79%). However, only 18 beneficiary decisions were made on the same day, while 119 were made one day after. Increasing the number of same-day phone call attempts may be a method to improve opt-in rates. Importantly, date and time data for contact attempts before a beneficiary decides to opt-in or opt-out were unavailable as of the time of the analysis. These data are captured and will be added to the analysis when available, which could provide more insight into whether a beneficiary is more likely to opt-in or opt-out.

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No description available.

Background: Ballad Health participates in the Centers for Medicare and Medicaid Services’ (CMS) Accountable Health Communities (AHC) model. The AHC model is evaluating if universal screening, referral, and navigation services for health-related social needs (HRSN) can improve outcomes and reduce unnecessary utilization and costs of health care services. To ensure the evaluation of the model has sufficient statistical power, navigation services are expected to be provided to a minimum number of individuals. The purpose of this study is to analyze the characteristics of Ballad Health’s AHC navigation services that could be modified to improve opt-in rates. Methods: The primary outcome measure was identified as whether a beneficiary contacted via telephone opted-in or –out of the navigation program. Andersen’s Behavioral Model for Health Service Use was used as the conceptual framework for selecting covariates of interest. Enabling factors were of primary interest because alternate interventions may be designed around them. Data was pulled for the time period of November 17, 2018 through February 14, 2019. Where possible, covariates were associated with data from CMS’ AHC Data Template v3.1 to accommodate replication for all AHC bridge organizations, though additional internally-collected data, which may not be available for all bridge organizations, were needed for some variables. Chi-squared tests were performed for each covariate. Results: No statistical differences were found for the primary covariates of interest. Opt-in rates by Navigator were lowest for Navigator 5 and highest for Navigator 4 (67.53% and 88.24%). Opt-in rates by weekday of decision were lowest on Thursdays and highest on Wednesdays (64.91% and 77.42%). Opt-in rates based on time of day were lowest between 8:00am and 9:59am, and highest between 12:00pm and 1:59pm (62.50% and 100%). Opt-in rates were lowest when the decision was made six days after the screening and highest when made the same day (53.57% and 83.33%). Opt-in rates were lowest when there were five weekdays between screening and navigation decision, and highest when there were three weekdays between the screening and decision (60% and 90%). Other non-process covariates of interest that were statistically significant for opt-in rates were the presence of either food, safety, or utility needs. Conclusions: Several groups had higher opt-in rates that were not statistically significant; small sample sizes may have impacted the significance of these differences. For example, opt-in rates were higher when made the same day as the screening than when made one day after (83.33% and 74.79%). However, only 18 beneficiary decisions were made on the same day, while 119 were made one day after. Increasing the number of same-day phone call attempts may be a method to improve opt-in rates. Importantly, date and time data for contact attempts before a beneficiary decides to opt-in or opt-out were unavailable as of the time of the analysis. These data are captured and will be added to the analysis when available, which could provide more insight into whether a beneficiary is more likely to opt-in or opt-out.

The primary objective of this research was to explore the impact of possible social factors on non-institutionalized adults 18 years of age or older residing in the United States who exhibited severe mental illness (SMI). A holistic sociological model was developed to explain SMI by incorporating elements of social learning theory, social disorganization theory, and gender socialization theory with social demographic factors. Based on the holistic sociological model, the following factors were investigated: demographic aspects of age, education, income and gender; gender socialization; influence of neighborhood area; social network influence based on communication and interaction among peers and family members; and socially deviant behaviors such as frequently smoking cigarettes, drinking alcohol and using drugs specifically marijuana. The impact of these factors on SMI was examined. A sample of 206 respondents drawn from National Survey on Drug Use and Health, 2003 was assessed. These respondents had answered all the questions related to SMI; social deviant behaviors; neighborhood environment; and communications among peers, family members and friends; and the other studied factors. Ordinary linear regression with interaction terms was employed as a statistical tool to assess the impact of social determinants on SMI. Being female, living a disorganized neighborhood, and frequent and high levels of smoking cigarettes and drinking alcohol had a significant influence on SMI. This reevaluation and reexamination of the role of gender socialization path, socially deviant behaviors like smoking and drinking, and community construction on SMI provided additional insights. This research is one of the first to develop a more holistic sociological model on SMI and explored the previously untested interactive relationships. The limitations of this study suggest the need to test a potential recursive research model and explore additional bi-directional associations.

In this dissertation, I aim to examine how policy packages focusing on improving reproductive conditions impact adverse maternal outcomes at the local level. I use the National Institute of Reproductive Health’s Local Reproductive Freedom Index (NIRH LRFI) to advance scholarship and policy advocacy on how policies included in that index may lessen the overall maternal mortality rate while also perhaps diminishing the disparity in maternal mortality between women of color and white women in 50 major U.S. cities. I argue that policy may be an effective—though under-studied—avenue for reducing the risk of maternal mortality because policies can be used to target specific social factors that research has demonstrated place individuals at higher risk of a negative outcome. However, these policy avenues usefully can be considered as interconnected policy packages rather than merely as single-policy interventions. Additionally, current research in this area suggests that there is a clear need for more research that combines SDH with policies aimed at improving health outcomes for reproducing individuals, particularly for communities of color (Creanga et al. 2015b; Kozhimannil, Vogelsang, and Hardeman 2015; Osypuk et al. 2015). As such, I situate maternal mortality, and specifically racial disparities in maternal mortality, among social determinants of health. Overall, I argue that to successfully decrease aggregate maternal mortality rates and lessen racial disparities in reproductive health, it is essential to center the experiences and needs of women of color and use a critical lens when evaluating measures assessing reproductive freedom.

Electronic nicotine delivery systems (ENDS) use, including e-cigarettes, has surpassed the use of conventional tobacco products. Emerging research suggests that susceptibility to e-cigarette use is associated with actual use among adolescents. However, few studies exist involving adolescents in high-risk, rural, socioeconomically distressed environments. This study examines susceptibility to and subsequent usage in school-going adolescents in a rural distressed county in Appalachian Tennessee using data from an online survey (N = 399). Relying on bivariate analyses and logistic regression, this study finds that while 30.6% of adolescents are ever e-cigarette users, 15.5% are current users. Approximately one in three adolescents are susceptible to e-cigarettes use, and susceptibility is associated with lower odds of being a current e-cigarette user (OR = 0.03; CI: 0.01–0.12; p < 0.00). The age of tobacco use initiation was significantly associated with decreased current use of e-cigarettes (OR = 0.89; CI: 0.83–0.0.97; p < 0.01). Overall, the results of this exploratory study suggest the need for larger studies to identify unique and generalizable factors that predispose adolescents in this high-risk rural, socioeconomically disadvantaged region to ENDS use. Nevertheless, this study offers insight into e-cigarette usage among U.S adolescents in rural, socioeconomically disadvantaged environments and provides a foundation for a closer examination of this vulnerable population.

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Objetivo. Estimar la desigualdad social en salud en la distribución de incidencia de COVID-19 en distritos de Lima Metropolitana y Callao, en el año 2020. Métodos. Se realizó un estudio ecológico a nivel distrital. Los datos se obtuvieron de diversas bases de datos institucionales. Se calcularon las métricas estándar, tales como la brecha absoluta y relativa, el índice de desigualdad de la pendiente, desigualdad proporcional e índice de concentración para analizar la relación entre varios indicadores de determinantes sociales y la incidencia de COVID-19 en 43 distritos de Lima Metropolitana y Callao en 2020, con el programa R Studio. Resultados. Este estudio demostró que, los distritos con mayor IDH, menor pobreza, mayor acceso a agua potable y alcantarillado presentan una mayor incidencia de COVID-19. Se calculó la desigualdad absoluta y relativa según los estratificadores sociales; sin embargo, estas son de mayor utilidad al compararse con los años. Conclusiones. Se determinó que los distritos con mayor IDH, menor pobreza monetaria, mayor acceso a agua potable y alcantarillado presentan una mayor incidencia de COVID-19. Esto se adjudicó al poco acceso a los servicios de salud y el pobre conocimiento de la enfermedad por parte de la población menos favorecida, lo cual se tradujo en un subdiagnóstico. Es probable que una actualización de los datos conlleve a una variación de los resultados. / Objective. Estimate social inequality in health in the distribution of incidence of COVID-19 in the districts of Lima Metropolitana and Callao, in 2020. Methods. This is an ecological study at the district level. The data were obtained from institutional databases. The relationship between four social determinants index and the incidence of COVID-19 in all the districts of Lima Metropolitana and Callao from March to June 2020 was evaluated, measuring the absolute and relative gaps, the slope index of inequality, proportional inequality and concentration index for each district, with R Studio program. Results. This study showed that the districts with higher HDI, lower poverty, greater access to potable water and sewerage system have a higher incidence of COVID-19. Absolute and relative inequality were calculated according to social stratifiers; however, these are more useful when compared with the years. Conclusion. It was determined that districts with higher HDI, lower monetary poverty, greater access to potable water and sewerage system have a higher incidence of COVID-19. This was attributed to poor access to health services and poor knowledge of the disease by the less favored population, that resulted in an underdiagnosis. An update of the data is likely to lead to a variation of the results. / Tesis

Health appears as a fundamental right on the Declaration of Human Rights of 1948 onarticle XXV. In humanitarian research, health is often viewed as a secondary theme anddid not have that much relevance. This has changed recently and nowadays health isconsidered an important factor. Social determinants of health are social factors, such ashousing, nutrition, and sanitation, that have a deep influence on one’s health. Thosedeterminants are particular to each individual and the conditions in which it lives and canbe modified by one’s migratory journey. In fact, migration in itself can be seen as a socialdeterminant of health, once the implications of the journey directly affect their health.Health inequity and the disparities caused by it are direct consequences of the socialdeterminants of health.This thesis analyses productions on refugee health in relation to the barriers existent inboth accessing and providing healthcare services to pregnant and postpartum women inGreece. The discussion chapter analyses the data found, discusses social determinants ofhealth, the resulting health disparities, and arguments through an intersectionality lens.The result chapter discusses the most important factors, suggests actions to overcome thebarriers and discusses the responsibility of the global society regarding inequalities.