Santiago Saldana

Depth estimation, spatially variant image registration, and super-resolution using a multi-lenslet camera

With a multi-lenslet camera, we can capture multiple low resolution (LR) images of the same scene and use them to reconstruct a high resolution (HR) image. For this purpose, two major computation problems need to be solved, the image registration and the super resolution (SR) reconstruction. For the first, one major hurdle is the spatially variant shifts estimation, because objects in a scene are often at different depths, and due to parallax, shifts between imaged objects often vary on a pixel basis. This poses a great computational challenge as the problem is NP complete. The multi-lenslet camera with a single focal plane provides us a unique opportunity to take advantage of the parallax phenomenon, and to directly relate object depths with their shifts, and thus we essentially reduced the parameter space from a two dimensional (x, y) space to a one dimensional depth space, which would greatly reduce the computational cost. As results, not only we have registered LR images, the estimated depth map can also be valuable for some applications. After registration, LR images along with estimated shifts can be used to reconstruct an HR image. A previously developed algorithm will be employed to efficiently compute for a large HR image in the size of 1024x1024.

Performance of Health Literacy Tests Among Older Adults with Diabetes

ABSTRACTBACKGROUNDKnowing a patient’s health literacy can help clinicians and researchers anticipate a patient’s ability to understand complex health regimens and deliver better patient-centered instructions and information. Poor health literacy has been linked with lower ability to function adequately in health care systems.OBJECTIVEWe evaluated and compared three measures of health literacy and performance among older patients with diabetes.DESIGNCross-sectional study utilizing in-person interviews conducted in participants’ homes.PARTICIPANTSA tri-ethnic sample (n = 563) of African American, American Indian, and white older adults with diabetes from eight counties in south-central North Carolina.MAIN MEASUREParticipants completed interviews and health literacy assessments using the Short-Form Test of Functional Health Literacy in Adults (S-TOFHLA), the Rapid Estimates of Adult Literacy in Medicine Short-Form (REALM-SF), or the Newest Vital Signs (NVS). Scores for reading comprehension and numeracy were calculated.RESULTSOver 90% completed the S-TOFHLA numeracy and approximately 85% completed the S-TOFHLA reading and REALM-SF. Only 73% completed the NVS. The correlation of S-TOFHLA total scores with REALM-SF and NVS were 0.48 and 0.54, respectively. Age, gender, ethnic, educational and income differences in health literacy emerged for several instruments, but the pattern of results across the instruments was highly variable.CONCLUSIONSA large segment of older adults is unable to complete short-form assessments of health literacy. Among those who were able to complete assessments, the REALM-SF and NVS performed comparably, but their relatively low convergence with the S-TOFHLA raises questions about instrument selection when studying health literacy of older adults.

Blood metabolite markers of preclinical Alzheimer's disease in two longitudinally followed cohorts of older individuals.

Recently, quantitative metabolomics identified a panel of 10 plasma lipids that were highly predictive of conversion to Alzheimers disease (AD) in cognitively normal older individuals (n = 28, area under the curve [AUC] = 0.92, sensitivity/specificity of 90%/90%).

Application of Random Forests Methods to Diabetic Retinopathy Classification Analyses

Background Diabetic retinopathy (DR) is one of the leading causes of blindness in the United States and world-wide. DR is a silent disease that may go unnoticed until it is too late for effective treatment. Therefore, early detection could improve the chances of therapeutic interventions that would alleviate its effects. Methodology Graded fundus photography and systemic data from 3443 ACCORD-Eye Study participants were used to estimate Random Forest (RF) and logistic regression classifiers. We studied the impact of sample size on classifier performance and the possibility of using RF generated class conditional probabilities as metrics describing DR risk. RF measures of variable importance are used to detect factors that affect classification performance. Principal Findings Both types of data were informative when discriminating participants with or without DR. RF based models produced much higher classification accuracy than those based on logistic regression. Combining both types of data did not increase accuracy but did increase statistical discrimination of healthy participants who subsequently did or did not have DR events during four years of follow-up. RF variable importance criteria revealed that microaneurysms counts in both eyes seemed to play the most important role in discrimination among the graded fundus variables, while the number of medicines and diabetes duration were the most relevant among the systemic variables. Conclusions and Significance We have introduced RF methods to DR classification analyses based on fundus photography data. In addition, we propose an approach to DR risk assessment based on metrics derived from graded fundus photography and systemic data. Our results suggest that RF methods could be a valuable tool to diagnose DR diagnosis and evaluate its progression.

Social Integration and Diabetes Management Among Rural Older Adults

Objectives: To describe diabetes management behaviors and social integration among older adults, and delineate the associations of social integration with diabetes management behaviors. Method: Interview data from 563 African American, American Indian, and White participants (age 60+) from eight south central North Carolina counties selected using a site-based procedure. Statistical analysis comprises descriptive statistics, bivariate analysis, and multivariate analysis. Results: Participants had high levels of social integration and largely adhered to diabetes management behaviors (glucose monitoring, checking feet, maintaining diet, formal exercise program, health provider monitoring A1C and examining feet). Social integration was associated with several behaviors; social network size, particularly other relatives seen and spoken with on the telephone, was associated with provider A1C monitoring and foot examinations. Discussion: Social integration had small but significant associations with diabetes management behaviors. This analysis suggests specific mechanisms for how social integration influences the effect of disease on disability.

The Association of Mental Conditions with Blood Glucose Levels in Older Adults with Diabetes

Objectives: People with diabetes must engage in several self-care activities to manage blood glucose; cognitive function and other affective disorders may affect self-care behaviors. We examined the executive function domain of cognition, depressive symptoms, and symptoms of generalized anxiety disorder (GAD) to determine which common mental conditions can co-occur with diabetes are associated with blood glucose levels. Methods: We conducted a cross-sectional in-person survey of 563 rural older adults (age 60 years or older) with diabetes that included African Americans, American Indians, and Whites from eight counties in south-central North Carolina. Hemoglobin A1C (A1C) was measured from a finger-stick blood sample to assess blood glucose control. Executive function, depressive symptoms, and symptoms of GAD were assessed using established measures and scoring procedures. Separate multivariate linear regression models were used to examine the association of executive function, depressive symptoms, and symptoms of GAD with A1C. Results: Adjusting for potential confounders including age, gender, education, ethnicity, marital status, history of stroke, heart disease, hypertension, diabetes knowledge, and duration of diabetes, executive function was significantly associated with A1C levels: every one-unit increase in executive function was associated with a 0.23 lower A1C value (p = 0.02). Symptoms of depression and GAD were not associated with A1C levels. Conclusions: Low executive function is potentially a barrier to self-care, the cornerstone of managing blood glucose levels. Training aids that compensate for cognitive impairments may be essential for achieving effective glucose control.

Cognitive function is a risk for health literacy in older adults with diabetes

AIMS Cognitive impairment is common in older adults with diabetes, yet it is unclear to what extent cognitive function is associated with health literacy. We hypothesized that cognitive function, independent of education, is associated with health literacy. METHODS The sample included 537 African American, American Indian, and White men and women 60 years or older. Measures of cognitive function included the Mini-Mental State Examination (MMSE), Verbal Fluency, Brief Attention, and Digit Span Backward tests. Health literacy was assessed using the S-TOFHLA. RESULTS Cognitive function was associated with health literacy, independent of education and other important confounders. Every unit increase in the MMSE, Digit Span Backward, Verbal Fluency or Brief Attention was associated with a 20% (p<.001), 34% (p<.001), 5% (p<.01), and 16% (p<.01) increase in the odds of having adequate health literacy, respectively. CONCLUSIONS These results suggest that cognitive function is associated with health literacy in older adults with diabetes. Because poor cognitive function may undermine health literacy, efforts to target older adults on improving health literacy should consider cognitive function as a risk factor.

Correlates of physician trust among rural older adults with diabetes.

OBJECTIVES To examine the demographic, health and diabetes management correlates of physician trust in a rural, multiethnic population with diabetes. METHODS 563 older (≥ 60 years) African American, American Indian and White adults completed in-home surveys, including the 11-item General Trust in Physicians Scale. RESULTS Higher trust scores were seen among: older (>75) participants (p < .01), those with fewer (<3) chronic health conditions (p < .01), and those who adhered to physical activity (p < .05) and dilated eye exam (p < .01) guidelines; the latter remained significant (eye exam, p = .019) or approached significance (physical activity, p = .051) after adjustment for potential confounders. CONCLUSIONS Physician trust may influence patient adherence to diabetes management recommendations. Efforts should be made to build trust in the patient-provider relationship to enhance patient outcomes.

Social Control in Older Adults’ Diabetes Self Management and Well-Being

The “dual effects” hypothesis argues that social control can be effective in promoting positive health-related behavior change, but it can also jeopardize the targeted individuals well-being. This hypothesis is tested using hemoglobin A1C as an objective indicator of behavioral compliance with diabetes self-management behavior and depressive symptoms. Differences in the effects of social control on A1C and depressive symptoms by sex and ethnicity are tested. Cross-sectional data were obtained from a multi-ethnic sample of older adults with diabetes (N = 593). Greater social control was associated with poorer rather than better odds of achieving glucose control, and with greater depressive symptoms. There was no evidence that social control has differential effects on either glucose control or depressive symptoms by sex or ethnicity. Active use of social control attempts by family members and friends, especially if they are coercive or punitive in nature, are likely counterproductive for maintaining the physical and mental health of older adults with diabetes.

Profiles of Food Security for US Farmworker Households and Factors Related to Dynamic of Change

OBJECTIVES We recruited 248 farmworker families with preschool-aged children in North Carolina and examined food security indicators over 24 months to identify food security patterns and examine the dynamic of change over time. METHODS Participants in the Niños Sanos study, conducted 2011 to 2014, completed quarterly food security assessments. Based on responses to items in the US Household Food Security Survey Module, we identified different states of food security by using hidden Markov model analysis, and examined factors associated with different states. We delineated factors associated with changes in state by using mixed-effect ordinal logistic regression. RESULTS About half of the households (51%) consistently stayed in the most food-secure state. The least food-secure state was transient, with only 29% probability of this state for 2 consecutive quarters. Seasonal (vs migrant) work status, having immigration documents (vs not documented), and season predicted higher levels of food security. CONCLUSIONS Heterogeneity in food security among farmworker households calls for tailoring intervention strategies. The transiency and unpredictability of low food security suggest that access to safety-net programs could reduce low food security risk in this population.