Relationship between Alcohol and Depression

Relationship between Alcohol and Depression The Complex Association between Alcohol Consumption and Depression Constantin Vintilescu Abstract Symptoms of depression are typically among those who abuse alcohol. Previous research has shown a positive correlation between alcohol consumption and depression exists; however, the exact nature of the association is complex. The purpose of this paper is to examine the relationship between the amount of alcohol consumption and severity of depression as described by the Alameda County Health and Ways of Living Study (ACHWLS) dataset. The raw data was aggregated, transformed, and used to calculate new variables. Correlation and curve estimation analysis was performed on the calculated variables. An overall positive correlation was confirmed, and previous research was upheld by demonstrating that abstainers and heavy drinkers have greater symptoms of depression than lite to moderate drinkers. However, a complex S-shaped pattern, with low symptoms of depression among very heavy drinkers, was determined to be the best fitting regression model. This finding has previously been undescribed , and may be due to the limitations of self-reporting by very heavy consumers of alcohol and the severely depressed. Further study is suggested, with screening performed by trained professionals, to confirm this finding. The Complex Association between Alcohol Consumption and Depression Alcohol consumption and depression are frequently co-occurring conditions. A cyclical pattern of escalating comorbidity has been described in people with both disorders, but previous studies have shown that the association is not a simple linear correlation. The aim of this paper is to conduct a secondary data analysis of the 1994 ACHWLS dataset to describe the relationship between alcohol abuse and depression. Research Questions Is there a positive correlation between alcohol consumption and depressive symptoms? Does a linear, quadratic, or cubic regression model explain the most variability between alcohol consumption and severity of depressive symptoms? Background Alcohol abuse is common and is often associated with depression. In a 2012 survey, conducted by the Substance Abuse and Mental Health Services Administration, of Americans over the age of twelve: 17.0 million reported heavy drinking, and 14.9 million were diagnosed with alcohol dependence (SAMHSA, 2013). According to the SAMHSA (2014), an estimated 43.7 million American adults experienced some form of mental illness; and an estimated 9.6 million adults had a serious mental illness, including major depression. Of these, 8.4 million people had co-occurring mental illness and a substance use disorder (SAMHSA, 2014). Of people with alcohol problems, 80% show symptoms of depression (Mclntosh Ritson, 2001), and 25% of those with depression also have an alcohol problem (Chick, 2002). An escalating cycle of comorbidity exists between alcohol abuse and depression: people with a mental health disorder have a higher likelihood of alcohol abuse when compared to people without mental illness (SAMHSA, 2014); people with concurrent major depression and a substance abuse disorder have more severe symptoms of depression than those without a substance abuse disorder (Ostacher, 2007); and greater severity of depression is associated with more drinking (Palfai et al., 2007). Thus, depression may augment alcohol use, which in turn, may increase symptoms of depression – creating an accumulative cycle of abuse and depression. Numerous studies confirm the positive association between alcohol consumption and depression (Alati et al., 2005; Dixit Crum, 2000; France et al., 2004; Hartka et al., 1991; Rodgers et al., 2000). However, the nature of the relationship is complex, as both the abstinence from and heavy consumption of alcohol are both associated with an increased risk of depression (Alati, et al., 2005; Blow, Serras, Barry, 2007; Rodgers et al., 2000). It is clear that the relationship is non-linear (Rodgers et al., 2000b), but there is uncertainty over its exact nature. Whether the association curve is J-shaped or U-shaped depends on the method of measurement (Graham, Massak, Demers, Rehm, 2007). Data Source The 1994 ACHWLS is part of a longitudinal funded by the National Institutes of Health, which began surveying a random sample of households in Alameda County, California in 1965. Alameda County was chosen because the diversity of residents closely resembled the population of the United States, and thus allowed for greater generalizability to the American public. The 1994 ACHWLS attempted to follow-up on all the respondents interviewed in 1965 and 1974 with a self-administered questionnaire regarding living patterns, health, and socio-demographics. No one question, on the ACHWLS, completely reflected the intensity of alcohol consumption, or the severity of depressive symptoms, exhibited by the respondent, so a strategy to represent a cumulative score for these variables was developed. Key questions regarding alcohol use and symptoms of depression were identified and aggregated in Table 1 and Table 2 respectively. Several responses to questions were reversed so that a higher numerical score reflected an increased severity of symptoms. Responses were subsequently transformed to a zero-based scale. Table 3 and Table 4 show the recoded values. Table 3 and Table 4 were each summated to calculate the new variables DRINKING and DEPRESSION respectively. Of the 2,729 respondents in the ACHWLS, only cases with complete responses to all items on both Table 2 and Table 3 were considered (N = 1,248). Included participants ranged in age from 46 to 95 years old (M = 63.1, SD = 9.79). The median income was $ 40,000-$44,999; the majority were male (56%); most had finished high school (90%), and had at least some higher education (66%). Race/ ethnic demographics are summarized in Table 1. Methods The distribution of DRINKING is slightly skewed to the right (skewness = 1.70). The histogram, mean score, and SD are shown in Figure 1. Possible values range from zero to 24. Similarly, DEPRESSION is also slightly skewed to the right (skewness = 1.48). The distribution is shown in Figure 2. Possible values range from zero to 18. The mean DEPRESSION score corresponding to each DRINKING value is shown in Figure 3. Being the sum of several ordinal values, it is important to clarify that DRINKING and DEPRESSION both represent continuous scales of intensity. Although their possible ranges are limited on this instrument, their values could theoretically be measured on an infinite positive scale of rational units. As such, parametric testing is appropriate; even though the data is not a perfectly normal distribution, parametric procedures are still valid because of the very large sample size (Ghasemi Zahedias, 2012). Pearson’s correlation was performed to test the overall relationship between quantity of alcohol consumption and of severity depression. Since convincing evidence has previously demonstrated a positive association exists between the variables in question, directional analysis was used to determine the p-value. This method increases the experiment’s statistical ability to discover an effect without changing the level of significance. To describe the nature of the association, non-linear regression was performed in SPSS. The curve was estimated using several models, including linear, quadratic, and cubic to determine the best fit. Results A directional Pearson Correlation was performed between DRINKING and DEPRESSION. The effect size was determined to be small, but significant, r (1249) = .091, p = .001. Curve fit analysis shows that linear (F 1, 1246 = 6.134, p = .013, R2 = .005), quadratic (F 2, 1245 = 7.789, p 2 = .011), and cubic (F 3, 1244 = 7.545, p 2 = .018) regression models were all significant. Figure 4 shows a comparison of all three models and the actual data. The cubic regression model was able to explain the highest degree of variability, accounting for .016 (adj. R2) of the variance in DEPRESSION. Figure 5 shows the cubic model along with the formula governing the curve. Discussion Although the Pearson’s correlation was a significant positive value, the effect size was small. This is an expected outcome because the nature of the association is non-linear. Negative correlations among abstainers and very lite drinkers served to negate much of the positive correlations among moderate and heavy drinkers. Segmented correlations or stepwise regression may be of value to determine the exact effect on depression for each of the following groups: abstainers, very lite drinkers, lite drinkers, moderate drinkers, heavy drinkers, and very heavy drinkers. Cubic regression was the best fitting curve, but only a small degree of variability was explained by this model. This may be due to possible limitations of the DEPRESSION variable. A more sensitive instrument to gauge depression severity may yield a higher R2. Also, due to the nature of very heavy drinking and severe symptoms of depression, persons with these afflictions may be under-represented in the ACHWLS survey. This possibility may be responsible for the low R-value. Screening performed by trained professionals may increase the correlation and variability explained by this model. Interestingly, the curve showed a complex S-shape, with very high alcohol consumption being associated with lower symptoms of depression. This observation may also be related to the sensitivity of the DEPRESSION variable, or it may be related to a reporting problem among very heavy drinkers. Although a maximum score of 24 is allowed by the screening instrument for DRINKING, the maximum score reported was only 18. This may suggest that very heavy drinkers were not accurately represented in the study data source. Very heavy drinkers and very depressed persons may not have responded to the survey or not have responded to all the items in Table 1 and Table 2 due to the nature of their alcohol problem or depression. A more focused study with screening performed by trained professionals may yield more accurate results than a generalized survey relying on self-reporting. Conclusion Correlations are weak; however, the over-all effect of alcohol consumption on depression is positive. The exact nature of the association is complex, with both heavy drinkers and abstainers showing greater symptoms of depression. The best fitting curve, for this dataset, is cubic with an S-shaped pattern. However, limitations among responders with very heavy drinking and severe symptoms of depression may be influencing the curvature. A focused study with screening performed by trained professionals is recommended. References Alati, R., Lawlor, D. A., Najman, J. M., Williams, G. M., Bor, W., OCallaghan, M. (2005). Is there really a J-shaped curve in the association between alcohol consumption and symptoms of depression and anxiety? Findings from the Mater-University Study of Pregnancy and its outcomes. Addiction, 100(5), 643-651. doi: 10.1111/j.1360-0443.2005.01063.x Blow, F. C., Serras, A. M., Barry, K. L. (2007). Late-life depression and alcoholism. Current Psychiatry Reports, 9(1), 14-19. Chick, J. (2002). Clinical depression in heavy drinkers of alcohol. Hospital Pharmacist, 9(1), 229-233. Dixit, A. R., Crum, R. M. (2000). Prospective study of depression and the risk of heavy alcohol use in women. The American Journal of Psychiatry, 157(5), 751-758. France, C., Lee, C. Powers, J. (2004), Correlates of depressive symptoms in a representative sample of young Australian women. Australian Psychologist, 39:228–237. doi:10.1080/00050060412331295054 Ghasemi, A. Zahedias, S. (2012). Normality tests for statistical analysis: A guide for non-statisticians. International Journal of Endocrinology and Metabolism, 10(2), 486-489. DOI:10.5812/ijem.3505 Graham, K., Massak, A., Demers, A., Rehm, J. (2007). Does the association between alcohol consumption and depression depend on how they are measured? Alcoholism, Clinical and Experimental Research, 31(1), 78-88. doi: 10.1111/j.1530-0277.2006.00274.x Hartka, E., Johnstone, B., Leino, E. V., Motoyoshi, M., Temple, M. T., Fillmore, K. M. (1991). A meta-analysis of depressive symptomatology and alcohol consumption over time. British Journal of Addiction, 86(10), 1283-1298. Mclntosh, C., Ritson, B. (2001). Treating depression complicated by substance misuse. Advances in Psychiatric Treatment, 7(1): 357-6. Ostacher, M. J. (2007). Comorbid alcohol and substance abuse dependence in depression: impact on the outcome of antidepressant treatment. The Psychiatric Clinics of North America., 30(1), 69-76. doi: 10.1016/j.psc.2006.12.009 Palfai, T. P., Cheng, D. M., Samet, J. H., Kraemer, K. L., Roberts, M. S., Saitz, R. (2007). Depressive symptoms and subsequent alcohol use and problems: a prospective study of medical inpatients with unhealthy alcohol use. Journal of Studies on Alcohol and Drugs, 68(5), 673-680. Rodgers, B., Korten, A. E., Jorm, A. F., Christensen, H., Henderson, S., Jacomb, P. A. (2000). Risk factors for depression and anxiety in abstainers, moderate drinkers and heavy drinkers. Addiction, 95(12), 1833-1845. doi: 10.1080/09652140020011135 Rodgers, B., Korten, A. E., Jorm, A. F., Jacomb, P. A., Christensen, H., Henderson, A. S. (2000b). Non-linear relationships in associations of depression and anxiety with alcohol use. Psychological Medicine, 30(2), 421-432. Substance Abuse and Mental Health Services Administration. (2013, September). Results from the 2012 National Survey on Drug Use and Health: Summary of National Findings, NSDUH Series H-46, HHS Publication No. (SMA) 13-4795. Rockville, MD: Author. Substance Abuse and Mental Health Services Administration. (2014, October 9). Mental and Substance Use Disorders. Washington, DC: Author. Retrieved February 15, 2015, from http://www.samhsa.gov/disorders Tables Table 1 ACHWLS Questions Related to Drinking Note: Responses appear exactly as they are reported in the 1994 ACHWLS. Table 2 ACHWLS Questions Related to Depression Note: Responses appear exactly as they are reported in the 1994 ACHWLS. Table 3 ACHWLS Questions Related to Drinking Recoded Note: Responses to items with IDs ending in â€Å"R† are recoded to reflect a higher severity with higher numerical value. All other responses appear exactly as they are reported in the 1994 ACHWLS. Table 4 ACHWLS Questions Related to Depression Recoded Note: Responses to items with IDs ending in â€Å"R† are recoded to reflect a higher severity with higher numerical value. All other responses appear exactly as they are reported in the 1994 ACHWLS. Table 5 Race / Ethnicity Demographics Note: Self-reported ethnicity / racial demographic data summarized from respondents of the 1994 ACHWLS who submitted complete responses to all question items listed on both Table 1 and Table 2. Figures Figure 1. Distribution of data for DRINKING variable. DRINKING is summation of responses listed in Table 3. Possible values range from 0 to 24. Figure 2. Distribution of data for DEPRESSION variable. DEPRESSION is summation of responses listed in Table 4. Possible values range from 0 to 18. Figure 3. Mean of all DEPRESSION scores corresponding to each DRINKING value. Figure 4. Best fit curve estimation for variables DRINKING and DEPRESSION. Mean data collected from the ACHWLS is shown in red. Figure 5. Cubic regression curve is given by the formula above. Mean data collected from the ACHWLS is shown in red.

Cafeteria Food Essay

Some people think that school cafeterias should be required to provide low-fat and/or vegetarian lunch options to accommodate the government’s nutritional guidelines, but all students do not eat the same. In my essay about cafeteria food, I will explain what students want in their food. I will also share the likes and dislikes in the food and what we can do to improve it. Cold pizza, undercooked hamburgers and brown lettuce sounds nasty. Many students from different states and districts have to deal with that every day at their schools. The food from the cafeterias used to be good, with a sweet flavor, but in the last few years the food had taken a different flavor, a flavor that cannot be tolerated by the students. The students need a better quality in their food, don’t they? It’s time for a change, a change to food, so students can be satisfied while their eating, a change to food that can be really worth it. As a matter of fact, we students pay money to the district every year and this is what we get? It’s time for a change. Everybody knows that eating healthy was from the past. However, now students and not necessarily just students eat fast food like McDonalds or Taco Bell. Now everybody does, which can be a good and a bad thing. The changes of the food’s taste has made many students to not eat at school, or even made students to bring lunch, and this as a matter of fact causes the school to lose money. In all actuality is the loss of money causing the school to give students poorly made food? If the schools would serve students food from places like Pizza Hut and Subway I can guarantee that most or all students would eat at the cafeterias and it would benefit to both school and students. These types of foods would be so great to have in schools, but unfortunately the district and the governments have taken action and unfairly, they have set down laws that prohibit these foods in the schools. They have settled these laws because they want us as students to eat healthy, but if they want to keep us healthy, why are they giving us food with a bad and nasty taste? Also, with these laws they keep students like me away from eating too much â€Å"junk† food because they can cause obesity. But do they keep on giving us food that is not even worth the amount of money our parents give to the government every year? If they were the ones who eat all of the nasty food given by the cafeteria they would of get tired of eating cold pizza and uncooked hamburgers with brown lettuce just like we the students do. In conclusion, we all understand what the overnment and the school system is trying to do with this healthy eating program. They want to promote healthy eating habits in an effort to prevent obesity. Although nutrition is important, students feel that there is a better way to have healthy food without ruining the taste. Therefore, in an effort to keep students healthy and leave with their stomachs satisfied we should all come together to realize that we are the ones that have to make it through the school day with the food.

The Development of Human Resource Management in China

Introduction HRM is short for Human Resource Management and identified as a new managerial science. HRM is originally invented by western academicians. Its history is not long and it has become more and more popular in the recent years. In Australia, HRM is replacing the old IR system as positive managerial action replaces â€Å"ground rules†. (Alexander, Lewer Gahan, 2008) In China, the business society has been significantly affected by â€Å"guanxi†. A mount of â€Å"ground rules† were created gradually in China’s business society. It means that after China’s reform and open policy had operated for 30 years, China still has not seriously developed a scientific managerial method for human resource. Thanks to the more and more frequently communication†¦show more content†¦However, HRM is not just appraisal. Now business people know that HR also creates value by increasing intelligence in the firm and HR professionals would translate their work into fi nancial performance. (Ulrich, 1997) Consequently, ten years earlier, HRM is more likely to be a department for people who got demotion to go. Business people’s understanding of HRM was not comprehensive enough. Therefore its theory and practice were not operated in a right way. It is fortunate today that this science has got more and more attention. People begin to realise its impact on the financial performance. It is a totally new area of management practice. Time for HRM dynasty has come. Q2 At what stage of development is the HR profession in China? Since 1978, China’s reform and open policy has taken. China’s marketing economy has only experienced 30 years. Unlike the developing history of HRM in the west, China would experience one certain stage in a very short period and shift to the next while in western country it might take several decades. 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Project Management, Nait Lrt Project - 1855 Words

Abstract The purpose of this research paper is to critically analyse the issues of delay in the NAIT LRT project. This project was initiated on September 6, 2015, which was supposed to be in 2014. The key objective of this research paper is to identify steps and activities that must be managed to avoid delays, development problems due to unexpected risks and even monitoring the project. This study will provide a brief analysis of the issues incurred in the failure of NAIT LRT project. This study undertakes the information available on the journal articles, newspaper articles, online material, City of Edmonton website and other news channel websites to explore the history and current position of the project. Keywords: Project†¦show more content†¦It is formally known as the Metro Line. (Objective) The 3.3km division line was planned to run from Churchill Square to NAIT linking MacEwan University, the Royal Alexandra Hospital, the new arena and Kingsway Mall to the main LRT system. It was expected to add 13,200-weekday riders to the city’s LRT network. The objective of this project is to get the line open, subject to it being safe, as described by city manager Simon Farbrother in an interview in Edmonton Journal. The construction and designing of this extended Meto Line were predicted to cost (baseline cost) of $755 million. This cost estimation includes the purchasing property and new LRVs. Sponsors of the NAIT LRT project were the Government of Alberta, the Government of Canada and City of Edmonton. The baseline schedule and milestones of this project are mentioned in the appendix of this paper. Thales Computer –Based Train Control system are responsible for operating the NAIT LRT. While construction on the $665 million (expected cost was $755 million) project was completed on schedule, there have been issues incorporating the new Communication-Based Train Control system with the current LRT signalling system. Mario Peloquin, Vice President, Tha les Rail Signalling Solutions Inc. says he appreciates everyone’s patience as crews work to complete the work. The current signalling system is one of the reasons for the delay of this project. The communication lines include the