Puberty is a critical stage in the human life course. Specifically, puberty is the physiological transitionary period that marks the beginning of human reproduction capability. These physiological changes are markedly different in boys and girls and accordingly typically occur on different timelines. Furthermore, these timelines are not a set guideline for every child as some may develop earlier and others later. This age of onset is a general range that has been observed and considered to be the normal age of onset. The normal age of onset of puberty is typically between ages 8 and 13 for girls or 9 and 14 for boys (Brito & Latronico, 2015, 93). Any development seen before these ages is typically defined as early onset and development seen after is late development. Professionals have further defined different stages of puberty in both boys and girls. In girls, this involves breast development, body hair growth, changes in height and weight, and menarche (Wheeler, 1991, pg. 2). In boys, pubertal development often involves genital growth, body hair growth, deepening of voice, and growth spurts (6). Puberty onset has been a popular topic of study in the health sciences as the age of onset can indicate the impact of biological and environmental factors. In particular, inequality in research of pubertal onset, the effects of early/delayed onset, and race/ethnicity is of special interest. For instance, there is a lack of existing literature on the role that culture plays on pubertal onset in Latino youth. This analysis intends to investigate the relationship between the integration of Hispanic culture in children’s lives and pubertal onset.

This thesis aims to advance healthcare and heart disease prevention by utilizing the Python programming language and various machine learning algorithms for heart disease detection. Being one of the main causes of death worldwide, cardiovascular disease is a serious global health concern. One person passes away from cardiovascular disease every 33 seconds in the United States alone. As the leading cause of death, early identification becomes critical for early intervention and prevention. The study addresses key research questions, including the role of machine learning in enhancing heart disease detection, comparative analysis of the six machine learning models, and the importance of predictive indicators. By leveraging machine learning algorithms for medical data interpretation, the thesis contributes insights into early disease detection.

It is well known that neighborhood contexts form an integral part in shaping development across the lifespan. At the same time, it is recognized that there is variability in the manner with which the neighborhood context is associated with pertinent outcomes, such as mental health and psychological well-being. In this regard, empirical research has differentiated between subjective and objective neighborhood indicators. Midlife is a critical life stage due to middle-aged adults being “sandwiched” between generations and being firmly entrenched in the workforce; in this regard, the neighborhood context could play a role in shaping mental health and psychological well-being in midlife. Of importance is determining which factors account for development in midlife, and whether individuals can find protective factors in order to preserve their health and psychological wellbeing into older adulthood. The purpose of this dissertation was to examine whether and how neighborhood context is associated with mental health and psychological well-being in midlife. The first study examined whether the subjective and objective neighborhood context moderates the impact of monthly adversity on mental health and psychological well-being in midlife. The second study aimed to examine whether and which potentially relevant latent factors exist among subjective and objective neighborhood indicators in a sample of middle-aged adults from the Phoenix Metropolitan area. Taken together, the results of these studies provide evidence that neighborhood context is indeed relevant resource for middle-aged adults. Specifically, in Paper 1, found that individuals who live in neighborhoods with less disorder show fewer steep declines in mental health and well-being in months when an adversity was reported. Paper 2 found that that there are distinct latent constructs that were primarily comprised of factors related to resource and prosperity and financial strain for the objective indicators. For subjective perceptions factors comprised neighborhood insights. These findings contribute to the literature on potential ways in which neighborhood context may serve as a resource and serve as the groundwork for future studies that test mechanisms linking the neighborhood context to mental health and well-being in midlife and inform future intervention studies.