Each summer, each interdisciplinary research group will explore at least one research area related to analyses using the WMH-CIDI data, or other related datasets (e.g., via our collaborators at KIIS). Proposed topics, including significance and approach, are described in the following sections. Importantly, students will be expected to identify, refine, and implement their own research topics under the supervision of the research mentors, and so the following examples should be viewed accordingly.
Post-Traumatic Stress Disorder in Ukraine
The turbulent history of Ukraine makes it a strong candidate for very high rates of trauma, potentially leading to high rates of post-traumatic stress disorder (PTSD). According to survey results, rates of trauma in Ukraine are very high with 29% of men reporting having seen someone die or be severely injured, 19% reporting having been severely beaten, 7% reporting having seen atrocities, and 4% of women reporting having been raped. Rates of self-report of traumatic events range from 0.4% to 40.0% across over 30 different traumas. Exploring the development and rates PTSD in a country with very high rates of traumatic events will lead to better understanding about population-based responses to pervasive trauma in the population. Possible approach: assess risk factors for PTSD, in particular looking at how different types of traumas may lead to different rates of post-trauma PTSD. Also explore the impact of PTSD on daily functioning.
Risk Factors for Distress
The Kessler Psychological scale (K6) in Ukraine. Non-specific psychological distress measures can be used to assess community well-being (Kessler et al., 2010). Such studies have been more widely completed in first world countries such as the United States and Australia (Kessler et al., 2010); few studies have been completed in the former Soviet Union (FSU; Bromet et al., 2005). In the United States, psychological distress has been correlated with perceived health (Tessler & Mechanic, 1978). In Australia, certain fields of employment have been associated with higher than average psychological distress (Punch & Tuettemann, 1990). Further research is needed to better understand distress in the FSU where residents are less able to meet basic needs and more likely to be exposed to trauma. The Ukraine provides an excellent example of an FSU country with its large and diverse population, a wealth of natural resources, but with political unrest and general upheaval since it became in independent country. Studies here may provide additional insight into the correlations between psychological distress and physical health or psychological distress and employment because the political and social upheaval present in the Ukraine is not a part of countries such as the United States and Australia. In the Ukraine there are higher rates of mental health problems (Bromet et al., 2005), which may affect how variables are correlated. Possible approach: Explore overall association between the K6 and a host of other variables including DSM diagnoses, demographics, and early childhood factors. In particular, explore the role of educational underemployment (e.g., someone with a Ph.D. driving a taxi as their main employment), ethnic displacement (e.g., ethnic Ukrainians living in predominantly Russian areas or vice versa), Chernobyl exposure (living in an area near Chernobyl at the time of the nuclear disaster) as well as risk factors shown to be associated with K6 in other countries with distress. Finally, consider outcomes associated with distress including physical and mental functioning.
Correlates of Subjective Health in Ukraine
Self-rated health is a strong and independent predictor of mental and physical issues (e.g., Shmueli et al., 1999), as well as mortality (Helmer et al., 1999). Trauma exposures (Kimerling et al., 2000) are particularly strongly associated with self-rated health. Given very high rates of trauma exposures in Ukraine, along with its extensive turbulent history, Ukraine is a unique country in which to assess subjective health correlates. Possible approach: Overall rates of ‘poor’ self-rated health are high (Men: 16.4%, Women: 26.3%) in the Ukraine-WMH sample. Students could assess overall levels of self-reported health in Ukraine across diverse demographic groups, followed by an extensive exploration of risk factors for poor health, including mental and physical health problems, traumas, demographic and childhood variables. Comparative analyses with other countries, including country adjustments for response styles which have been shown to have strong cross-country differences (Jurges, 2007) will be an important aspect of this analysis.
Co-morbidity of Smoking and Alcohol
Previous investigation by our group has identified very high rates of heavy alcohol consumption (including binge drinking), including rates higher than in national surveys in Russia (Webb et al., 2005). National rates of alcoholism are also some of the highest in the world (Bromet et al., 2005). Furthermore, smoking rates and nicotine dependence among men are very high, with a rapid recent increase in smoking in women (Webb et al., 2007). Co-morbid smoking and alcohol heavy use and/or dependence can lead to significant health implications (Falk et al., 2006). Possible approach: Evaluate the patterns of co-morbid alcoholism and nicotine use, by exploring globally identified risk factors, as well as risk factors potentially unique to the country of Ukraine. Also, explore the disability of individuals with both smoking and alcohol disorders.
A Dyadic Model of Partner Violence in Ukraine
In the United States, many studies have shown that partner violence is committed equally by both men and women, but this is ignored in theory, treatment, and general public perception (Straus, 2011). Recognizing the gender symmetry in partner violence will allow for the development of treatments based on actual risk factors (Straus, 2009) as well as allowing research to move forward in better understanding women committers (Langhinrichsen-Rohling, 2005). Studying dyads, looking at couples and who commits violence in the relationship, rather than studying individuals, will allow for a clearer picture of the development of partner violence (Straus, 2011). Further research has established links between dyad violence and depression (Straus & Winstok, 2013) and inter-generational transmission of violence (Langhinrichsen-Rohling, 2005). Expanding this research to the Ukrainian sample will allow for further exploration of similarities and differences with patterns of violence in the United States, as well as exploration of dyadic partner violence in high smoking and alcohol use communities. Possible approach: Assess dyadic violence through the use of questions regarding whether the participant is violent toward their partner and whether the partner is violent towards the participant. Assess risk factors for different patterns of intimate partner violence. An important part of this research will be cross-national comparisons, including with the United States.