Impact of Genetic Testing and Family Health History Based Risk Counseling on Behavior Change and Cognitive Precursors for Type 2 Diabetes.

Family health history (FHH) in the context of risk assessment has been shown to positively impact risk perception and behavior change. The added value of genetic risk testing is less certain. The aim of this study was to determine the impact of Type 2 Diabetes (T2D) FHH and genetic risk counseling on behavior and its cognitive precursors. Subjects were non-diabetic patients randomized to counseling that included FHH +/- T2D genetic testing. Measurements included weight, BMI, fasting glucose at baseline and 12 months and behavioral and cognitive precursor (T2D risk perception and control over disease development) surveys at baseline, 3, and 12 months. 391 subjects enrolled of which 312 completed the study. Behavioral and clinical outcomes did not differ across FHH or genetic risk but cognitive precursors did. Higher FHH risk was associated with a stronger perceived T2D risk (pKendall < 0.001) and with a perception of "serious" risk (pKendall < 0.001). Genetic risk did not influence risk perception, but was correlated with an increase in perception of "serious" risk for moderate (pKendall = 0.04) and average FHH risk subjects (pKendall = 0.01), though not for the high FHH risk group. Perceived control over T2D risk was high and not affected by FHH or genetic risk. FHH appears to have a strong impact on cognitive precursors of behavior change, suggesting it could be leveraged to enhance risk counseling, particularly when lifestyle change is desirable. Genetic risk was able to alter perceptions about the seriousness of T2D risk in those with moderate and average FHH risk, suggesting that FHH could be used to selectively identify individuals who may benefit from genetic risk testing.

Cervical cancer precursors and hormonal contraceptive use in HIV-positive women: application of a causal model and semi-parametric estimation methods.

OBJECTIVE: To demonstrate the application of causal inference methods to observational data in the obstetrics and gynecology field, particularly causal modeling and semi-parametric estimation. BACKGROUND: Human immunodeficiency virus (HIV)-positive women are at increased risk for cervical cancer and its treatable precursors. Determining whether potential risk factors such as hormonal contraception are true causes is critical for informing public health strategies as longevity increases among HIV-positive women in developing countries. METHODS: We developed a causal model of the factors related to combined oral contraceptive (COC) use and cervical intraepithelial neoplasia 2 or greater (CIN2+) and modified the model to fit the observed data, drawn from women in a cervical cancer screening program at HIV clinics in Kenya. Assumptions required for substantiation of a causal relationship were assessed. We estimated the population-level association using semi-parametric methods: g-computation, inverse probability of treatment weighting, and targeted maximum likelihood estimation. RESULTS: We identified 2 plausible causal paths from COC use to CIN2+: via HPV infection and via increased disease progression. Study data enabled estimation of the latter only with strong assumptions of no unmeasured confounding. Of 2,519 women under 50 screened per protocol, 219 (8.7%) were diagnosed with CIN2+. Marginal modeling suggested a 2.9% (95% confidence interval 0.1%, 6.9%) increase in prevalence of CIN2+ if all women under 50 were exposed to COC; the significance of this association was sensitive to method of estimation and exposure misclassification. CONCLUSION: Use of causal modeling enabled clear representation of the causal relationship of interest and the assumptions required to estimate that relationship from the observed data. Semi-parametric estimation methods provided flexibility and reduced reliance on correct model form. Although selected results suggest an increased prevalence of CIN2+ associated with COC, evidence is insufficient to conclude causality. Priority areas for future studies to better satisfy causal criteria are identified.

Using Image Processing Methods to Improve the Detection of Buried Explosive Threats in GPR Data

Current state of the art techniques for landmine detection in ground penetrating radar (GPR) utilize statistical methods to identify characteristics of a landmine response. This research makes use of 2-D slices of data in which subsurface landmine responses have hyperbolic shapes. Various methods from the field of visual image processing are adapted to the 2-D GPR data, producing superior landmine detection results. This research goes on to develop a physics-based GPR augmentation method motivated by current advances in visual object detection. This GPR specific augmentation is used to mitigate issues caused by insufficient training sets. This work shows that augmentation improves detection performance under training conditions that are normally very difficult. Finally, this work introduces the use of convolutional neural networks as a method to learn feature extraction parameters. These learned convolutional features outperform hand-designed features in GPR detection tasks. This work presents a number of methods, both borrowed from and motivated by the substantial work in visual image processing. The methods developed and presented in this work show an improvement in overall detection performance and introduce a method to improve the robustness of statistical classification.