acase@tulane.edu

acase@tulane.edu

acase@tulane.edu

acase@tulane.edu

acase@tulane.edu

acase@tulane.edu

acase@tulane.edu

Women in rural western Kenya experience depression, yet few formal treatment options exist. What other options for support are available to these African women suffering from depression? How do these women navigate this “therapeutic landscape” of modern and traditional care? What is the role of social capital, including faith-based and community-based networks? I used a mixed methods case study approach to explore how women in Siaya, Kenya experience depression and navigate the therapeutic landscape – the forms of health provision as understood by the women who use them – to deal with poor mental health. I conducted in-depth interviews with women suffering from depression, members of their social networks, and key informants, ranging from clinicians and healers, to community elders, depression survivors, and community group and religious leaders. I used focus group discussions to elicit contextual information and daily mobile phone diaries to collect information on small, day-to-day health actions and social network interactions. I encountered a “treatment desert” shaped by an inadequate government health system, a deteriorating indigenous healing system degraded by Christianity and modernity, and a religious healing tradition that is considered unacceptable by most women in the study site. This therapeutic landscape is rocky and difficult to navigate and low social cohesion limits the support a woman receives from her in-laws, extended family, friends, group members, and neighbors. While churches and community groups are more reliable in times of need, financial and time barriers limit their utility for promoting mental health. Given this landscape, women’s responses to depression are predominantly inward-focused, consisting of prayer, keeping quiet, and staying busy. I suggest interventions that offer lay delivery of proven therapies and build collective social capital to address this chronic burden of poor mental health among rural African women. Ultimately, the low social cohesion seen in my study is rooted in material poverty and gender inequality, including oppressive and restrictive marriages. Efforts to build the social capital women need to tackle depression should be accompanied by attention to these structural factors that degrade social cohesion. / acase@tulane.edu

Background Smoking poses a risk for increased morbidity and mortality from many diseases. The ethnic group of American Indians has the highest prevalence of smokers compared to other racial/ethnic groups in the United States, and therefore may be at a greater risk of smoking-related death. This study aimed to estimate all-cause mortality attributable to smoking, and specifically to examine the association of smoking and mortalities from cardiovascular disease (CVD) and cancer in American Indians using data from the Strong Heart Study. Methods The Strong Heart Study is a large, community-based, prospective survey of CVD risk factors in American Indians from in three tribes residing in North and South Dakota, seven tribes in southwestern Oklahoma, and three tribes in central Arizona. A total of 4549 tribal members aged 45 to 74 years, representing 62% of total population in this age group, were recruited from all eligible individuals at the baseline survey between July 1989 and January 1992. The participation rates were 55% in the Dakota center, 62% in the Oklahoma Center, and 72% in the Arizona center. Baseline data were collected using personal interview and a physical examination. Mortality and causes of death were followed up from 1993 through the end of 2009. The numbers of deaths attributable to smoking until the end of 2009 were estimated using multivariable-adjusted relative risk analysis stratified according to gender and center. Results Overall, there was a significant association between smoking and death from any cause after adjustment for multiple risk factors. In 2009, a total number of 363 deaths were found attributable to smoking among American Indian population aged 45-74, of which 105 deaths were among men and 258 deaths among women. There is a significant association smoking and CVD mortality only in Dakota center. In cancer deaths, there was a significant association both in men and women: a total of 1462 deaths were attributable to smoking among American Indian population aged 45-74, of which 571in men, and 891in women. Conclusions Smoking was associated with increased risk of death among American Indians, especially with cancer deaths. There was no overall association between smoking and CVD deaths, but a significant association was observed in Dakota center. Reducing the prevalence of smoking might benefit in prevention of smoking related mortality among American Indians. / acase@tulane.edu

Background: Resampling methods such as the Monte Carlo (MC) and Bootstrap Approach (BA) are very flexible tools for statistical inference. They are used in general in experiments with small sample size or where the parametric test assumptions are not met. They are also used in situations where expressions for properties of complex estimators are statistically intractable. However, the MC and BA methods require relatively large random samples to estimate the parameters of the full permutation (FP) or exact distribution. Objective: The objective of this research study was to develop an efficient statistical computational resampling method that compares two population parameters, using a balanced and controlled sampling design. The application of the new method, the balanced randomization (BR) method, is discussed using microarray data where sample sizes are generally small. Methods: Multiple datasets were simulated from real data to compare the accuracy and efficiency of the methods (BR, MC, and BA). Datasets, probability distributions, parameters, and sample sizes were varied in the simulation. The correlation between the exact p-value and the p-values generated by simulation provide a measure of accuracy/consistency to compare methods. Sensitivity, specificity, power function, false negative and positive rates using graphical and multivariate analyses were used to compare methods. Results and Discussions: The correlation between the exact p-value and those estimated from simulation are higher for BR and MC, (increasing somewhat with increasing sample size), much less for BA, and most pronounced for skewed distributions (lognormal, exponential). Furthermore, the relative proportion of 95%/99% CI containing the true p-value for BR vs. MC=3%/1.3% (p<0.0001) and BR vs. BA=20%/15% (p<0.0001). The sensitivity, specificity and power function of the BR method were shown to have a slight advantage compared to those of MC and BA in most situations. As an example, the BR method was applied to a microarray study to discuss significantly differentially expressed genes. / acase@tulane.edu