Experiences of Internalized and Enacted Stigma among Women with Obstetric Fistula in Tanzania

Background: Obstetric fistula is the development of a necrosis between the bladder and the vagina and/or the bladder and the rectum as a result of prolonged obstructed labor, resulting in urinary or fecal incontinence. In Tanzania surgical repair for obstetric fistula is provided freely by the government but it is estimated that there are over 25,000 women living with an untreated fistula. These women experience high degrees of psycho-social stresses exacerbated by the stigma surrounding their condition. There is a dire need to explore stigma within this population in order to better understand its impact, as stigma affects both treatment seeking behavior as well as long term recovery of those who access surgical repair.

Study Aims: This study aims to understand the experiences of stigma among women with obstetric fistulas by examining both internalized and enacted stigma, and by identifying pertinent correlates of internalized stigma.

Methods: This mixed-methods study utilized both quantitative and qualitative data collected in two related studies at a single hospital in Moshi, Tanzania. All study participants were women receiving surgical repair for an obstetric fistula. In the quantitative portion, cross-sectional survey data were collected from 52 patients. The primary outcome was fistula-related stigma, measured using an adaptation of the HASI-P stigma scale, which included constructs of both internalized and enacted stigma. In the qualitative portion, 45 patients participated in a semi-structured in-depth interview, which explored topics such as stressors caused by the fistula, coping mechanisms, and available support. The transcripts were analyzed using analytic memos and an iterative process of thematic coding using the framework of content analysis.

Results: Expressions of internalized stigma were common in the sample, with a median score of 2.1 on a scale of 0 – 3. Internalized was significantly correlated with negative religious coping, social participation, impact of incontinence and enacted stigma. Qualitative analysis was consistent and demonstrated widespread themes of shame and embarrassment. Experiences of enacted stigma were not as common (median score of 0), although some items, like those pertaining to mockery and blame, were endorsed by up to 25% of the study sample. Themes of anticipated stigma (isolation and non-disclosure due to the possibility of stigmatization) were also evident in the qualitative sample and may explain the low enacted stigma scores observed.

Conclusion: In this sample of women receiving surgical repair for an obstetric fistula, stigma was evident, with internalized stigma resulting in psychological impacts for patients. Experiences of both anticipated and enacted stigma were also observed. There is a need to explore interventions that would decrease stigma while also increasing support for these women, as stigma may be a barrier towards accessing surgical repair and reintegration following surgery.

Keywords: Tanzania, obstetric fistula, stigma, maternal health

Robust Uncertainty Quantification and Scalable Computation for Computer Models with Massive Output

Uncertainty quantification (UQ) is both an old and new concept. The current novelty lies in the interactions and synthesis of mathematical models, computer experiments, statistics, field/real experiments, and probability theory, with a particular emphasize on the large-scale simulations by computer models. The challenges not only come from the complication of scientific questions, but also from the size of the information. It is the focus in this thesis to provide statistical models that are scalable to massive data produced in computer experiments and real experiments, through fast and robust statistical inference.

Chapter 2 provides a practical approach for simultaneously emulating/approximating massive number of functions, with the application on hazard quantification of Soufri\`{e}re Hills volcano in Montserrate island. Chapter 3 discusses another problem with massive data, in which the number of observations of a function is large. An exact algorithm that is linear in time is developed for the problem of interpolation of Methylation levels. Chapter 4 and Chapter 5 are both about the robust inference of the models. Chapter 4 provides a new criteria robustness parameter estimation criteria and several ways of inference have been shown to satisfy such criteria. Chapter 5 develops a new prior that satisfies some more criteria and is thus proposed to use in practice.