Faculty Bibliography

BACKGROUND/AIMS: Stroke is a leading cause of premature death and disability, and increasing the proportion of individuals who are aware of stroke symptoms is a target objective of the Healthy people 2020 project. METHODS: We used data from the 2014 Supplement of the National Health Interview Survey (NHIS) to assess the prevalence of stroke symptom knowledge and awareness. We also tested, using a logistic regression model, the hypothesis that individuals who have knowledge of all 5 stroke symptoms will be have a greater likelihood to activate Emergency Medical Services (EMS) if a stroke is suspected. RESULTS: From the 36,697 participants completing the survey 51% were female. In the entire sample, the age-adjusted awareness rate of stroke symptoms/calling 911 was 66.1%. Knowledge of the 5 stroke symptoms plus importance of calling 911 when a stroke is suspected was higher for females, Whites, and individuals with health insurance. Stroke awareness was lowest for Hispanics, Blacks, and survey participants from Western US region. CONCLUSION: The findings allude to continuing differences in the knowledge of stroke symptoms across race/ethnic and other demographic groups. Further research will confirm the importance of increased health literacy for Stroke management and prevention in minority communities.

BACKGROUND: The global prevalence of skin depigmentation/skin bleaching among blacks, estimated at 35%, is on the rise and is associated with a host of negative health and medical consequences. Current etiological approaches do not fully capture the emotional and psychological underpinnings of skin bleaching. The current study investigated the potential mediating role of depression, or post-traumatic stress symptoms (avoidance and hyperarousal) on the relationship between childhood physical and sexual abuse (CPSA) and skin bleaching. METHODS: A total of 1226 university participants (ages 18-30 years and 63.4% female) from three Caribbean countries (Jamaica, Barbados, and Grenada) provided data for the current analysis. They all completed self-reported measures of general demographic information along with the short screening scale for posttraumatic stress disorder (DSM-IV), childhood trauma, and skin bleaching questions. RESULTS: The prevalence of skin bleaching in our study was 25.4%. Our findings showed that individuals who bleached their skin were more likely to have been abused as children (21.6% versus 13.5%, p<0.001), were more likely to have significant symptoms of trauma (34.1% versus 24.0%, p=0.005), and were more likely to have significant depression (43.7% versus 35.1%, p=0.032). We found that trauma-related hyperarousal symptoms positively mediated the relationship between childhood physical and sexual abuse and skin bleaching (Indirect Effect=0.03, p<0.05), while avoidance (Indirect Effect=0.000, p>0.05) and depressive (Indirect Effect=0.005, p>0.05) symptoms did not. CONCLUSION: The presence of trauma symptoms and childhood physical and sexual abuse (CPSA) may increase the likelihood of skin bleaching. Findings suggest that further exploration is needed to ascertain if the presence of skin bleaching warrants being also screened for trauma.

Introduction: There is growing evidence suggesting relationships between depression and insomnia symptoms, yet scant evidence indicating directionality of those relationships or potential mediators. Evidence also suggests that social connectedness is a vital, protective factor for depression. Social connectedness may hold promise for helping sleep scientists better understand relationships between disrupted sleep and depression symptomology. Methods: The current study drew on social network analysis, an underexplored approach in sleep medicine, and survey methods to examine social connections, insomnia severity, and depression symptoms. Participants (n = 38) were 44.7% female, with an average age of 56.7 years; 86.8% of the participants self-identified as black. Bivariate correlations and logistic regression were performed to examine relationships between social connectedness, insomnia severity, and depression symptoms. Results: Of the sample, 71.1% reported insomnia and 23.7% reported depression. Participants provided responses to social network items across kin (m = 2.8 people), non-kin (m = 2.4 people), and formal networks (m = 1.6 people). Case by case agreement was strong between kin network size and depression symptomology (chi square < .05), but not between kin network and insomnia (chi square = .658). The logistic regression in the current study showed individuals with depressive symptoms were 6.75 (95% CI 1.45-31.47, p < 0.) times more likely to have severe insomnia versus individuals without clinically significant depression symptoms. There was no significant relationship in the regression between network variables and insomnia or depression symptoms. Conclusion: Our findings are consistent with previous findings and evidence on a strong, positive relationship between depression symptomology and insomnia severity. However, they are not in line with literature suggesting a positive relationship between kin networks and depression symptomology. It is of interest to explore the causal relationship between social connectedness and sleep, and how social networks might serve as a protective (or risk) factor for insomnia, and maybe depression

Introduction: Sleep is an important domain of health. Most data come from US or European samples. This study explored sleep in a university student population in Jamaica. Methods: As part of an ongoing study, N = 361 students were administered the Sleep Practices and Attitudes Questionnaire. To broadly evaluate differences between men and women t-tests for continuous variables and chi-square tests for categorical variables were evaluated. Results: Women reported a greater sleep need on average (7.97h vs 7.57h; p = 0.047). They were also more likely to feel tired (78% vs 67%; p0.019) and share their bed with other family members (19% vs 3%; p < 0.0001). If they felt sleepy, women were more likely to nap (p = 0.002) or exercise (p = 0.028). Women reported worse stimulus control, with greater likelihood of reading (p = 0.0002), worrying (p = 0.0001), arguing (p = 0.0065), and working (p = 0.0086) in bed. Men were more likely to get sleep information from the community (p = 0.022) and trust this information (p = 0.023), as well as information from neighbors (p = 0.007). Men were more likely to consider sleep an important safety issue for pilots (p = 0.0007), drivers (p = 0.013), and less important for police (p = 0.014). Women were more likely to believe that it was important to keep a consistent bedtime (p = 0.030), that st ress affects their sleep (p = 0.006), and that sleep is impor tant for health (p = 0.008). Women were more likely to believe that sleep loss impairs sex drive (p = 0.0008) and makes you tired (p = 0.012). Men were more likely to consider sleep important if it could be shown to be related to driving (p = 0.003), and heart disease (p = 0.0007). Women were more likely to consider sleep important if it could be shown to be related to weight (p = 0.047), hypertension (p = 0.046), missed work (p = 0.019), cognitive performance (p = 0.030), concentration (p = 0.006), and diabetes (p = 0.025). Conclusion: Overall, women reported a greater sleep need and more positive beliefs about sleep, but worse sleep hygiene

Introduction: rist actigraphy has been used extensively to measure objective sleep duration in sleep-related research. It has been validated against polysomngraphic and self-reported sleep measures. We sought to compare the accuracy of sleep measurements produced by three algorithms developed for wrist actigraphic scoring. Methods: A random sample of 263 participants were selected from among those participating in the Research on Obesity and Type 2 Diabetes among African Migrants (RODAM) study in Kumasi, Ghana. Each participant completed a sleep diary and wore a wrist actigraph for a period of seven days. Actigraphic data were scored using Actilife software, and th ree sepa rate validated algorithms developed separately by Sadeh, Cole-Kripke, and Jean-Louis were applied. SPSS was used to compare actigraphic sleep durations, derived from each algorithm, with self-reported sleep durations. Results: Valid actigraphic data, defined as having data for at least 5 of the 7-day period, were collected from 255 participants. Total sleep time in minutes varied substantially by sleep algorithm. Average sleep time derived from the Sadeh, Cole-Kripke, and Jean-Louis' algorithms were 346.8 (SD 49.3), 320.1 (SD 53.8), and 453.4 (SD 68.1), respectively. Participants self-reported an average of 468.3 (SD 85.7) minutes of sleep per night. The Sadeh and Cole-Kripke algorithms classified only 2.7% and 6.8% of the sample as sleeping the recommended 7 to 8 hours, respectively, whereas Jean-Louis' classified 28.2% in the 7 to 8 hour range. Conclusion: When employing actigraphy for sleep duration measurement, care should be exercised in choosing the most appropriate algorithm for scoring actigraphic data based on specific study populations in order to increase the accuracy of study results. The Jean-Louis' algorithm seems to fare better than other actigraphic scoring algorithms

Introduction: Older age is generally characterized by increased risk for chronic conditions, such as obesity, dyslipidemia, diabetes, and hypertension, and significant changes in sleep patterns. It is unclear whether sleep duration (short or long sleep), contributes to chronic conditions differentially contrasting retirees (> 65 years) and non-retirees (18-65 years). Methods: The study utilized data from the 2004-2013 National Health Interview Survey. NHIS applies a stratified multistage sample survey of the resident civilian non-institutionalized US population. Respondents provided sociodemographic and physician-diagnosed chronic conditions. We defined an unhealthy cohort as a subset of the retired population who reported at least one of four chronic conditions: obesity, dyslipidemia, diabetes, and hypertension. The healthy cohort included individuals who reported none of these conditions. Data was analyzed using SPSS 20. Results: Of the sample, 56.4% of the retirees were female and 81.7% were white. Among non-retirees, 52.0% were female and 76.5% were white. Non-retirees and retirees had an average sleep duration of 7.08 and 7.49, respectively (p < .01). Adjusted logistic regression analysis indicated that overall retirees were less likely to report short sleep (< 7hrs) [OR = .92, 95%CI = .89-.95, p8hrs) [OR = 1.89, 95%CI = 1.801.98, p < .01] compared to non-retirees. Healthy retirees had a 41% greater odds of reporting long sleep, but were no more or less likely to report short sleep, compared to non-retirees. Unhealthy retirees had a two-fold greater odds of reporting long sleep, but 5% lower odds of reporting short sleep, relative to non-retirees. Conclusion: Retirees had a higher mean sleep duration and were characterized by significantly greater odds of long sleep compared to non-retirees regardless of health status. Although retirees overall were more likely to report long sleep, those with 1 or more chronic health conditions had greater odds of reporting long sleep duration compared to healthy retirees

Introduction: Although the association between sleep disturbance and cancer is well documented, there is little evidence regarding how sleep duration among cancer survivors may be associated with other chronic diseases. Growing evidence suggests that cancer and diabetes may share common risk factors such as age, gender, race, being over weight, physical inactivity, smoking and alcohol. However, it is yet unclear how unhealthy sleep duration (a known cardiometabolic risk factor) may affect the relationship between cancer and diabetes. The aim of this study was to investigate whether sleep duration moderated the relationship between physician-diagnosed cancer and diabetes. Methods: Data was extracted from the NHIS dataset (2004-2013), providing demographics, chronic diseases and sleep duration. For the present analysis, we used a subset of individuals providing complete data for the following variables: physician-diagnosed cancer and diabetes and self-reported habitual hours of sleep. Data were analyzed to assess the moderating effect of sleep duration on cancer and diabetes risk. Results: Of the total sample of 283,086 participants, 15.8% were black and 77.2% were white; 55.7% were female and the mean age was 47.7 (18.0) years. In the first adjusted regression model, short sleep duration [< 7 hours] (Beta = 0.15, p < .001) and cancer (Beta = 0.91, p8 hours] (Beta = 0.28, p < .001) and cancer (Beta = 0.14, p < .001) were independently associated with diabetes. However, moderation analysis indicated that only long sleep significantly moderated relationships between cancer and diabetes (Beta = -0.218, S.E. = 0.055, p < .0001, 95% CI = -0.326-0.110). Short sleep did not significantly moderate those relationships. Conclusion: Our findings demonstrate significant associations of short and long sleep with cancer and diabetes. We should note that among people with long sleep, having a cancer diagnosis did not increase diabetes r isk. However, among people with a cancer diag nosis, short sleep seemed to have increased diabetes risk

Introduction: There is growing use of electronic cigarettes (e-cigarettes) as an alternative to cigarette smoking. However, little is known about the cardiometabolic consequences of e-cigarette use. The current study therefore evaluated the associations between e-cigarette use, and diabetes as well as the interactions between e-cigarette use, and difficulty falling asleep. Methods: We used data from the 2014 National Health Interview Survey (NHIS) supplement. The NHIS is an in-person household survey that provides estimates on health indicators, health care utilization and access, and health-related behaviors of the civilian, non-institutionalized US population. The main independent variable for the study was use of electronic cigarettes. Description of demographics was performed for the studied population using survey frequency procedures. The moderating role of sleep on electronic cigarette and diabetes was also assessed using interaction effects. Results: From the 36,521 survey participants included in the study, mean age = 49.3 years +/- 0.10 (+/- SEM), mean BMI kg/m(2) = 27.9 +/- 0.03. Of the entire sample, 52% were female; White 66.9%; 11.9% Black; 5.6% Asian; and Hispanics 15.6%. The rate of electronic cigarette use was 12.8%. Two percent of these individuals used electronic cigarettes alone, 10.8% were combined e-cigarette users and cigarette smokers, 28% st r ict non-electronic cigarette smokers, and 59.4% non-e-cigarette use/non-smoker. After multivariate logistic regression analysis, strict electronic cigarette users had lower odds of having diabetes than nonelectronic-cigarette users/non-smokers, OR [95% CI] = 0.33 [0.160.69]; p = 0.01. There was a significant interaction effect for str ict users of electronic cigarettes and difficulty falling asleep on diabetes (beta estimate = 0.7428, p < 0.01). There were no significant interactions for strict non-electronic-cigarette smokers and difficulty falling asleep on diabetes (beta estimate = 0.2558, p < 0.10). Conclusion: The use of e-cigs lowered the risk of diabetes, compared to non-electronic-cigarette users/non-smokers. However, among those strict e-cig users who have difficulty falling asleep, there was increased association with diabetes. Further studies are needed to confirm these findings and to examine the underling mechanisms of these associations. Given the overall serious and well established health hazards, we do not endorse any form of smoking

Introduction: Previous research has shown that Blacks/African-Americans (vs Non-Hispanic Whites) are more likely to be obese, suffer from stroke, and experience short sleep(SS8hrs/day) durations. Also, sleep duration itself is related to obesity and stroke risk, and the relationship between sleep duration and obesity is stronger in Blacks/African-Americans. This study explored the mediating role of obesity on relationships of SS and LS with stroke, while also contrasting traditional and newer multivariate machine modeling approaches. Methods: Data from the National Health Interview Survey from 20042013 (N = 288,888) was used. Structural equation modeling (SEM) and Bayesian Belief Network (BBN) analysis assessed the mediating effects of BMI on the relationship between SS, LS, and stroke, and whether race/ethnicity differences in obesity moderated relationships. Covariates included age, gender, marital status, and income. Results: Based on SEM results, BMI positively mediated relationships between SS and stroke (Path Coefficient Estimates < 0.027;p < 0.001), and between LS and stroke (Path Coefficient Estimates = 0.024; p < .001), adjusting for covariates. In SEM, race/ethnicity did not significantly moderate relationships between SS or LS and obesity. In contrast, BBN analysis showed that these relationships differed between blacks and whites. Blacks who were SS and obese had a 5.14% stroke probability, while white counterparts had a 3.73% stroke probability, with a significant difference of 37.8% (p < 0.001). Blacks who were LS and obese had an 11.71% stroke probability compared to whites with an 8.66% stroke probability and a significant difference of 35.21% (p < 0.001). Conclusion: No racial/ethnic influences on the mediating effect of BMI on the sleep-CVD relationship were detected using SEM. However, BBN analysis (but not SEM) showed racial/ethnic influences on the mediating effect of BMI on the sleep-stroke relationship, suggesting that obese blacks who reported short or long sleep were at greater risk for stroke. Findings also highlight the power of BBN analysis to elucidate disparities in complex chronic diseases

Introduction: Insomnia is a highly prevalent sleep disorder in the U.S., and it is associated with employment status, gender, income, and education. While low socioeconomic status is linked to complaints of insufficient sleep, little is known, however, about the predictive effect of certain social determinants of insomnia among blacks. Our study examined the relationship between different socioeconomic variables and presence of insomnia among blacks. Methods: The study utilized data from the Metabolic Syndrome Outcome (METSO) study. This is an NIH-funded cohort study, enrolling a sample of 1,035 participants (mean age = 59 +/- 13 years), who provided complete data for the analysis. Of the sample, 69.20 % were male and 30.80 % were female; 42.54% had annual family income < $10K, 64.69% reported High School Education level, and 37.71% indicated a U.S. birthplace. The. Logistic regression analysis was used to assess the relationship between self-reported insomnia (a composite of difficulty falling asleep, difficulty staying asleep, and/or early morning awakening) and socioeconomic factors. Results: Analysis showed that income was the only factor showing significant associations with insomnia after controlling for education level, birthplace, age, and gender. Based on the logistic regression analysis, blacks with annual family income < $10,000 had increased odds of reporting insomnia (OR = 1.685; 95 CL 1.07-2.65, p $10,000. These results remained significant even after controlling for covariates. Conclusion: Consistent with previous studies, blacks reporting lower family income were more likely to report insomnia symptoms. Thus, income may play an important role in understanding higher rates of sleep complaints among blacks