association between elevated liver enzymes and metabolic ...sensitivity c-reactive protein (crp, an...

Association Between Elevated Liver Enzymes and MetabolicSyndrome Among Thai Adults

1Sajithya Perera, 1,2Vitool Lohsoonthorn, 2Wiroj Jiamjarasrangsi, 2Somrat Lertmaharit & 1Michelle A. WilliamsSajithya Perera, Vitool Lohsoonthorn, Wiroj Jiamjarasrangsi, Somrat Lertmaharit & Michelle A. WilliamsMIRT Program, University of Washington, Seattle, WA, and Chulalongkorn University, Bangkok, Thailand

Introduction Results • Metabolic syndrome (MetS) is a collection of symptoms, such as excessive abdominal fat, insulin resistance, elevated blood pressure and dyslipidemia, that increases the risk of coronary heart disease and type 2 diabetes

• Positive linear trends were observed between elevated AST, ALT, and ALK concentrations and the presence of increasing numbers of metabolic abnormalities

ASTQ2Q3Q4

M e tS r isk acco rd in g to v aryin g co n ce n tra tio n s o fliv e r marke rs am o n g me n

that increases the risk of coronary heart disease and type 2 diabetes.

• Increasing numbers of studies indicate positive associations between liver enzymes, including γ-glutamyl transferase (GGT), aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALK), and high-sensitivity C-reactive protein (CRP, an inflammation marker), and metabolic disorders.

• While the risks of MetS have been recognized in developed countries, there is limited awareness of the risks and prevalence of metabolic disorders in the Asia-Pacific region. More specifically, the relationship between elevated liver

abnormalities.

•Significant correlations between the liver markers AST, ALT and ALK and MetS components (including triglycerides and waist circumference) were observed.

• After adjusting for potential confounders, men in the upper quartiles of ALT and women in the upper quartiles of ALK were at an increased risk for MetS outcome when compared to those with concentrations in the 1st

quartile. th

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To assess the extent to which, if at all, there are associations between elevated liver enzymes and MetS in a population of Thai adults receiving

Objective

Discussion

Asia Pacific region. More specifically, the relationship between elevated liver enzymes and MetS remains unexplored. Men with ALK concentrations in the 4th quartile had a 3.72-fold

increased risk of MetS (95% CI 1.49-9.29).

Women in the 4th quartile of ALT had a 2.55-fold increased risk of MetS (95% CI 1.22-5.35)

• For both men and women, increasing quartiles of AST concentration were not statistically significantly associated with increased risks of MetS.

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M e tS r isk acco rd in g to v aryin g co n ce n tra tio n s o fliv e r marke rs am o n g w o me n

annual health exams.

Table 1. Socio-demographic and clinical characteristics of study participants.

Men Women Characteristics (N=451) (N=940) na % na % Age (Years)

35-40 116 25.7 169 18.0 40-49 193 42.8 454 48.3 50-59 127 28.2 305 32.4 ≥60 15 3.3 12 1.3

Mean ± SD 45.7 ± 7.3 46.3 ± 6.5 Education

<Bachelor degree 129 29.1 170 18.3 Bachelor degree 90 20.3 388 41.7 Master degree 88 19.8 217 23.3

Table 2. Evaluation of mean (± standard deviation) concentrations of liver markers in relation to number of metabolic abnormalities used

to define metabolic syndrome.

Liver Marker Number of Metabolic Abnornalities Trend Test

P-value 0 1 2 3 ≥4

Men n = 133 n = 142 n = 81 n = 58 n = 26

AST (units/l) 27.4 ± 22.5 27.9 ± 21.2 28.7 ± 9.8 30.7 ± 11.9 35.2 ± 22.1 0.059

ALT (units/l) 27.7 ± 18.6 33.6 ± 50.5 38.9 ± 22.8 44.6 ± 30.2 47.6 ± 28.1 <0.001

AST : ALT ratio 1.1 ± 0.3 1.0 ± 0.4 0.9 ± 0.4 0.8 ± 0.3 0.8 ± 0.2 <0.001

ALK (units/l) 66.8 ± 18.0 70.1 ± 20.8 70.2 ± 18.1 74.5 ± 17.4 79.6 ± 21.4 0.001

Women n = 427 n = 261 n = 130 n = 68 n = 32

AST (units/l) 21.8 ± 11.0 22.1 ± 8.3 23.5 ± 8.8 23.6 ± 9.0 33.8 ± 19.3 <0.001

ALT (units/l) 17.4 ± 8.9 20.1 ± 15.1 23.1 ± 12.6 26.8 ± 15.8 50.4 ± 41.7 <0.001

AST : ALT ratio 1.4 ± 1.0 1.3 ± 0.5 1.2 ± 0.5 1.0 ± 0.3 0.8 ± 0.3 <0.001

• These findings add to an emerging body of literature that suggests elevated liver enzymes may be related with MetS risk.

• The ease and non-invasive nature of obtaining liver markers from patients at risk for MetS, makes the incorporation of liver markers in diagnosing and predicting MetS a promising and feasible possibility.

• Prospective studies are needed to more fully determine the practical value of elevated liver enzymes as a clinical risk predictor of MetS and related disorders among Thai adults

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MethodsMaster degree 88 19.8 217 23.3 PhD degree 137 30.9 156 16.8

Previous smoker 81 18.1 34 3.7 Current smoker 87 19.4 15 1.6 Never drinker 227 50.7 794 85.4 <10 g/day 174 38.8 128 13.8 10-30 g/day 32 7.1 8 0.9 >30 g/day 15 3.3 0 0.0 Components of MetS

Waist circumference (cm) 85.2 ± 9.1 74.7 ± 9.6 Triglyceride (mg/dl) 156.9 ± 136.2 92.9 ± 47.9 HDL-cholesterol (mg/dl) 53.4 ± 13.4 64.9 ± 16.0 Systolic blood pressure (mmHg) 126.5 ± 17.3 118.9 ± 16.9 Diastolic blood pressure (mmHg) 79.0 ± 10.9 71.9 ± 10.5 Fasting plasma glucose (mg/dl) 94.7 ± 28.4 87.4 ± 15.6

Liver markers

AST (units/l) 28.9 ± 19.3 22.7 ± 10.4 ALT (units/l) 35.5 ± 35.5 20.8 ± 15.4 AST : ALT ratio 1.0 ± 0.4 1.3 ± 0.8 ALK (units/l) 70.3 ± 19.4 63.0 ± 18.1

• Conducted a cross-sectional study of 1,608 patients (451 men and 940 women) who participated in annual health examinations at King Chulalongkorn Memorial Hospital in Bangkok, Thailand (December 2006 - February 2007).

• Participants underwent routine clinical physical examinations (collection of venous blood samples and measurement of height, weight, waist circumference and resting blood pressures).

• Eligible participants were asked to provide information about their age, marital status,

ALK (units/l) 57.6 ± 15.1 65.4 ± 18.8 68.6 ± 16.8 72.7 ± 22.7 74.2 ± 16.2 <0.001

Table 3. Spearman correlation coefficients from analysis of associations of liver markers

with metabolic abnormalities that define metabolic syndrome.

Liver markers WC TG HDL-C SBP DBP FPG

Men

AST 0.160 b 0.230 b -0.014 0.132 b 0.100 a 0.103 a

ALT 0.340 b 0.356 b -0.220 b 0.160 b 0.140 b 0.228 b

AST : ALT ratio -0.379 b -0.339 b 0.321 b -0.140 b -0.134 b -0.247 b

ALK 0 093 a 0 179 b -0 100 a 0 109 a 0 086 0 093 a

among Thai adults.

• Study limitations include:

The results are not generalizable to the general Thai population because of a unique study population

Some error and resulting residual confounding is possible due to self-reporting and the lack of quantitative measures of behavioral characteristics such as smoking and drinking,.

Absence of rigorous measures of liver health such as the use of liver

This research was supported by Rachadapiseksompoj of Medicine Research Fund, Chulalongkorn University and an award from the National Institutes of Health (T37-MD001449). The authors wish to also thank the staff of the Preventive Medicine Clinic, King Chulalongkorn Memorial Hospital in Bangkok, Thailand for their assistance in data collection.

aNumber may not be added up to the total number due to missing data occupation, educational attainment, medical history, use of anti-hypertensive, anti-

diabetic, or lipid lowering medications, smoking status, alcohol consumption habits, and physical activity.

• Alanine aminotransferase (ALT), aspartate aminotransferase (AST), and alkaline phosphatase (ALK) were measured using standard clinical methods.

• Statistical analysis (frequency distributions, trend tests, Spearman’s rank correlations, univariate and multivariable logistic regressions) were performed separately for men and women to determine associations between liver enzymes and components of MetS.

ALK 0.093 0.179 -0.100 0.109 0.086 0.093

Women

AST 0.106 b 0.175 b 0.013 0.050 0.026 0.006

ALT 0.310 b 0.284 b -0.178 b 0.189 b 0.117 b 0.150 b

AST : ALT ratio -0.373 b -0.276 b 0.268 b -0.230 b -0.154 b -0.210 b

ALK 0.362 b 0.247 b -0.084 a 0.233 b 0.179 b 0.206 b

a p < 0.05, b p < 0.001 WC = waist circumference; TG = triglyceride; HDL = high-density lipoprotein; SBP = systolic blood pressure; DBP = diastolic blood pressure; FPG = fasting plasma glucose

Absence of rigorous measures of liver health, such as the use of liver biopsies, (to determine hepatic steotosis inflammation, and fibrosis).

Exposure to hepatotoxic chemicals and biological agents that may have influenced liver inflammation and therefore, liver marker concentrations were also not taken into account.

Risk of Dyslipidemia in Relation to Level of Physical Activity among Thai Professional and Office Workers

1Cherell L. Dancy, 2Vitool Lohsoonthorn, and 1Michelle A. Williams1MIRT Program, University of Washington, Seattle WA, USA; and

2Department of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailandp , y , g y, g ,

Background Materials and MethodsA large epidemiological, clinical, and experimental literature clearly pointsto favorable physiological changes and overall health benefits associatedwith regular physical activity. For instance, Evidence from studies primarilyconducted in North America and Europe indicate that physiological effectsof physical activity include improved insulin sensitivity, reduced blood

ResultsWe conducted a cross-sectional study of 1,608 professional and office workers (536 men and1,072 women) who participated in annual health examination at Chulalongkorn MemorialHospital in Bangkok, Thailand.

Participants were asked to provide information about their age, marital status, occupation,

Overall, this is a well-educated population, with more than 70% of thepopulation having at least a bachelor’s degree. Approximately 44% ofmen reported having low levels of physical activity, while 26% reportedhigh levels of physical activity. Over 51% of women reported lowlevels of physical activity with only 14.2% reporting high levelsp y y p y

pressure, decreased concentrations of pro-inflammatory cytokines inperipheral circulation, reduced oxidative stress and improved plasma lipidand lipoprotein concentrations.

Although several investigators have evaluated influences of lifestylecharacteristics on metabolic parameters in Thai adults, none has assessed theextent to which, if at all, habitual physical activity is associated withfavorable lipid profiles among Thai men and women.

p p g peducational attainment, medical history, smoking status, and alcohol consumption habits.Information concerning physical activity was collected using a self-administered Global PhysicalActivity Questionnaire (GPAQ). Then, participants underwent routine physical examinations andovernight fasting venous blood samples were collected to determine participants’ lipid profiles.

Multivariable least squares linear regression procedures were used to assess the influence ofphysical activity on serum lipid and lipoprotein concentrations. Logistic regression procedures toestimate the relative risks of having clinically relevant high TCH (≥200 mg/dl), high TG (≥ 150mg/dl) concentrations and low HDL-C concentrations (<40 in men and <50 in women) according

physical activity (Table 1)

After controlling for confounders, men who reported high physicalactivity levels had on average a 3.42 mg/dl higher (p=0.020) in HDL-Cconcentrations, when compared to men who reported low physicalactivity levels. Higher mean HDL-C concentrations were also observedfor women who reported high physical activity levels, when comparedwith their less active counterparts (4.24 mg/dl, p=0.004). TGconcentrations were 30.92 mg/dl lower in men (p=0.029) and 12.83

Table 1. Socio-demographic characteristics of study participants.

to their habitual physical activity level (low, moderate and high activity levels).Objectives

To evaluate the relation between physical activity levels and fasting serumconcentrations of total cholesterol (TCH), triglyceride (TG), high densitylipoprotein-cholesterol (HDL-C), and the total cholesterol: high densitylipoprotein-cholesterol (TCH:HDL-C) ratio.

mg/dl lower in women (p=0.003) who had high physical activity levelswhen compared with less active individuals (Table 2)

Men who reported high physical activity levels, compared with thosewho reported low physical activity levels, had a 59% reduction in riskfor hypertriglyceridemia (OR=0.41, 95% CI: 0.24-0.70). Thecorresponding OR for women was 0.43 (95%CI: 0.21-0.88). Noassociation was found between physical activity levels and TCHconcentrations (Table 3).

Table 2. Relationships of serum lipids and lipoprotein concentrations with levels of habitual physical activity: estimated linear regression coefficients (β), standard errors (SE), and p-values.

Levels of Physical Activity

Total Cholesterol (mg/dl)

Triglyceride (mg/dl)

HDL-Cholesterol (mg/dl)

Total Cholesterol: HDL-C Ratio

β1 ± SE p-value β1 ± SE p-value β2 ± SE p-value β3 ± SE p-valueAmong Men

Low Reference Reference Reference Reference

Moderate -0.15 ± 4.27 0.971 -12.24 ± 13.48 0.364 0.92 ± 1.40 0.511 -0.10 ± 0.14 0.456 High 3.73 ± 4.48 0.405 -30.92 ± 14.13 0.029 3.42 ± 1.46 0.020 -0.29 ± 0.14 0.043

Trend test p-value 0.439 0.030 0.023 0.045

Adjusted R2 1.89% 9.18% 7.62% 8.05%

Men Women Characteristics (N=536) (N=1072)

n* % n* % Age (Years)

35-40 124 23.1 171 16.0 40-49 217 40.5 479 44.7 50-59 171 31.9 402 37.5 ≥60 24 4.5 20 1.9

Mean (SD)** 46.5 (7.6) 47.2 (6.8) Education

less than Bachelor degree 155 29.3 209 19.7 Bachelor degree 106 20.0 437 41.1

DiscussionOverall, these data suggest that habitually active men and women areless likely to have hypertriglyceridemia and low HDL-C concentrations.Our study confirms the favorable effects of physical activity on lipid andlipoprotein concentrations among Thai professional and office workers.These are consistent with the evidence documenting the cardiovascularhealth benefits of a physically active lifestyle.

β1 ± SE p-value β4 ± SE p-value β1 ± SE p-value β3 ± SE p-valueAmong Women

Low Reference Reference Reference Reference

Moderate -1.43 ± 2.60 0.583 -10.35 ± 3.16 0.001 3.01 ± 1.07 0.005 -0.20 ± 0.07 0.003 High -1.97 ± 3.57 0.581 -12.83 ± 4.32 0.003 4.24 ± 1.47 0.004 -0.28 ± 0.09 0.002

Trend test p-value 0.507 <0.001 0.001 <0.001 Adjusted R2 5.57% 14.60% 11.57% 13.17%

1 Coefficients adjusted for age (categorical), education, cigarette smoking, alcohol consumption and body mass index (categorical). 2 Coefficients adjusted for age (categorical), education, alcohol consumption and body mass index (categorical). 3 Coefficients adjusted for age (categorical), education and body mass index (categorical). 4 Coefficients adjusted for age (categorical), education, cigarette smoking and body mass index (categorical).

Table 3 Odds ratio (OR) and 95% confidence interval (CI) of the risk of dyslipidemia according to levels of physical activitygMaster degree 110 20.8 245 23.1 PhD degree 158 29.9 171 16.1

Cigarette Smoking Status

Never smoker 328 61.7 1009 95.1 Previous smoker 108 20.3 36 3.4 Current smoker 96 18.0 16 1.5

Alcohol Consumption Status

Non-current drinker 269 50.5 909 85.8 Current drinker 264 49.5 150 14.2

Body Mass Index (kg/m2)

health benefits of a physically active lifestyle.

Several important limitations must be considered when interpreting theresults of our study. First, as the study population was comprised ofhighly educated middle-aged university employees, the results may notbe generalizable to the general Thai population. Second, we used self-reported physical activity to classify study participants. Therefore, wecannot exclude the possibility that some misclassification may haveoccurred. Further, because of this cross-sectional data collection design,we cannot be certain of the temporal relation between level of physical

Table 3. Odds ratio (OR) and 95% confidence interval (CI) of the risk of dyslipidemia according to levels of physical activity.

Levels of Physical Activity

High TCH High TG Low HDL-C High TCH:HDL-C Ratio

Adjusted OR1 (95%CI) Adjusted

OR2 (95%CI) Adjusted OR1 (95%CI)

Adjusted OR3 (95%CI)

Among men Low 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Moderate 0.95 (0.61-1.47) 1.01 (0.63-1.60) 0.85 (0.46-1.58) 1.15 (0.74-1.79) High 1.28 (0.80-2.06) 0.41 (0.24-0.70) 0.61 (0.30-1.23) 0.76 (0.47-1.22)

Trend Test p-value 0.358 0.003 0.171 0.338

Adjusted OR3 (95%CI) Adjusted

OR4 (95%CI) Adjusted OR3 (95%CI) Adjusted

OR3 (95%CI) Body Mass Index (kg/m ) <18.5 5 0.9 47 4.4 18.5-24.9 301 56.8 652 61.6 25.0-29.9 176 33.2 263 24.9 ≥30 48 9.1 96 9.1

Mean (SD)** 25.1 (3.5) 24.0 (4.2) Level of Physical Activity

Low 228 43.6 526 51.1 Moderate 157 30.0 358 34.8 High 138 26.4 146 14.2

*Number may not be added up to the total number due to missing data ** SD = Standard Deviation

p p yactivity and risk of dyslipidemia. Inferences concerning the protectiveeffect of physical activity and dyslipidemia, however, would beenhanced with data from prospective studies and randomized clinicaltrials.

This research was supported by Rachadapiseksompoj Faculty of Medicine Research Fund, Chulalongkorn University, Thailand and National Institutes

of Health award (T37-MD001449), USA.

OR OR OR

OR

Among women Low 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) 1.00 (Reference) Moderate 0.98 (0.74-1.32) 0.72 (0.47-1.10) 0.69 (0.47-1.00) 0.72 (0.52-1.00) High 0.82 (0.56-1.21) 0.43 (0.21-0.88) 0.63 (0.37-1.07) 0.76 (0.48-1.19)

Trend Test p-value 0.400 0.011 0.028 0.077

High TCH = TCH ≥200 mg/dl; High TG = TG ≥150 mg/dl; Low HDL-C = HDL-C<40 mg/dl in men and HDL-C<50 mg/dl in women; High TCH:HDL-C ratio = TCH:HDL-C ratio >4.5 in men and TCH:HDL-C ratio >4 in women. 1 Adjusted for age (categorical), education, alcohol consumption and body mass index (categorical). 2 Adjusted for age (categorical), education, cigarette smoking, alcohol consumption and body mass index (categorical). 3 Adjusted for age (categorical), education and body mass index (categorical). 4 Adjusted for age (categorical), education, cigarette smoking and body mass index (categorical).

Comparison of Waist Circumference, Body Mass Index and Percent Body Fat in Identifying Cardiovascular Disease Risk among Thai Adults

1Linda Paniagua 2Vitool Lohsoonthorn 2Somrat Lertmaharit 2Wiroj Jiamjarasrangsi and 1Michelle A WilliamsLinda Paniagua, Vitool Lohsoonthorn, Somrat Lertmaharit, Wiroj Jiamjarasrangsi, and Michelle A. WilliamsMIRT Program, University of Washington, Seattle WA, USA; and 2Department of Preventive and Social Medicine, Faculty of Medicine, Chulalongkorn University, Bangkok, Thailand

Obesity has reached epidemic levels with at least 400 million adults classified as being obese in 2005. Cardiovascular diseases (CVD) have been the leading cause of death in Thailand

Introduction and Objectives Figure I Receiver Operating Characteristic (ROC) curves with area under curve (AUC) and 95% confidence intervals of BMI, %BF, WC, WHR and WHtR for predicting CVD risk factors among Thai men.

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BMI %BF WC WHR WHtR

(kg/m2) (%) (cm)

Men

Table 2. Spearman's rank correlation coefficients for anthropometric measurements and CVD risk factors.

since 1987. Between 1985 and 1997 the prevalence of heart disease in Thailand tripled to 168 per 100,000 population.

Obesity has been proven to be a strong and consistent risk factor for CVD, though the best way to measure obesity in the context of clinical and population-based studies has become increasingly controversial. Waist circumference (WC), waist hip ratio (WHR) and waist height ratio (WHtR) are used as measures of central obesity, while body mass index (BMI) and percent body fat (%BF) are generally used as measures of overall obesity. It remains unclear however weather BMI or other measures are most strongly associated with cardiovascular risk factors in Asian populations.

In light of heterogeneity of previous study findings and potential contraindication of generalizing results across populations, we sought to examine the relationship of measures of

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---- BMI: ROC Area = 0.68 (95%CI: 0.63-0.73)---- %BF: ROC Area = 0.70 (95%CI: 0.66-0.75)---- WC: ROC Area = 0.64 (95%CI: 0.59-0.70)---- WHR: ROC Area = 0.67 (95%CI: 0.62-0.72)---- WHtR: ROC Area = 0.67 (95%CI: 0.62-0.73)

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Elevated Triglyceride Low HDL-Cholesterol Fasting Plasma Glucose (mg/dl) 0.178** 0.215** 0.202** 0.291** 0.224**

Triglyceride (mg/dl) 0.312** 0.374** 0.282** 0.330** 0.329**

HDL-C (mg/dl) -0.370** -0.377** -0.350** -0.299** -0.358**

Systolic Blood Pressure (mmHg) 0.287** 0.318** 0.315** 0.306** 0.312**

Diastolic Blood Pressure (mmHg) 0.222** 0.271** 0.229** 0.272** 0.249**

Women

Fasting Plasma Glucose (mg/dl) 0.320** 0.329** 0.339** 0.300** 0.327**

Triglyceride (mg/dl) 0.389** 0.404** 0.419** 0.382** 0.420**

HDL-C (mg/dl) -0.380** -0.362** -0.357** -0.289** -0.351**

S t li Bl d P ( H ) 0 371** 0 358** 0 368** 0 286** 0 358**g g p p g poverall obesity (BMI and %BF) and central obesity (WC, WHR and WHtR) with CVD risk factors among Thai adults.

Materials and Methods

This cross-sectional study comprises of 1,391 Thai participants (451 men and 940 women) who had annual health exam in a mobile health clinic. The prevalence of cardiovascular disease risk factors was determined according to tertile of each anthropometric measure. Spearman’s rank correlation was used to determine the association of the five anthropometric indices with metabolic parameters including fasting plasma triglyceride, high density lipoprotein, glucose and blood pressure. Receiver operating characteristic (ROC) curves were plotted to compare

Results

• TABLE 1: In both genders, the prevalence of CVD risk factors were increased across successive tertiles for each anthropometric measure.

• TABLE 2: Among men, 3 metabolic parameters (i.e., triglyceride (r=0.374), high density lipoprotein-cholesterol (r=-0.377) and systolic blood pressure (r=0.318)) were most strongly correlated with %BF. Fasting plasma glucose (r=0.291) and diastolic blood pressure (r=0.272)

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Elevated Fasting Plasma Glucose Elevated Blood Pressure

Systolic Blood Pressure (mmHg) 0.371 0.358 0.368 0.286 0.358

Diastolic Blood Pressure (mmHg) 0.325** 0.324** 0.314** 0.224** 0.300**

**Correlation coefficients are significant at the 0.001 level. Note: body mass index (BMI), percent body fat (%BF), waist circumference (WC), waist to hip ratio (WHR) and waist to height ratio (WHtR)

anthropometric measure as predictors of the prevalence of cardiovascular risk factors. We calculated the area under the curve as a summary estimate of discrimination.

g p g ( ) p ( )were most strongly correlated with WHR. BMI was least correlated with fasting plasma glucose (r=0.178), systolic (r=0.287) and diastolic (r=0.222) blood pressure.

• Among women, BMI, a measure of overall adiposity, was most strongly associated with 3

metabolic parameters (high density lipoprotein (r=-0.380), systolic (r=0.371) and diastolic blood pressure (r=0.325)) in women. Conversely, among the remaining of four measures of adiposity, WHR was least correlated with metabolic parameters.

• Review of ROC curves (Figure 1) indicated that BMI performed at least as well as the other

measures of adiposity in identifying risk of dyslipidemia and elevated blood pressure among men. BMI was slightly less predictive of elevated fasting plasma glucose (AUC = 62%, 95%CI 54-69%) than were %BF (AUC = 66%, 95%CI 59-73%) and WHR (AUC = 69%, 95%CI 63-75%) In women BMI performed at least as well or better in predicting all CVD risk factors

Figure II Receiver Operating Characteristic (ROC) curves with area under curve (AUC) and 95% confidence intervals of BMI, %BF, WC, WHR and WHtR for predicting CVD risk factors among Thai women.Results

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Elevated Triglyceride Low HDL-Cholesterol

Table 1. Prevalence of CVD risk factors in relation to varying degree of adiposity as assessed using different anthropometric measures.

Cardiovascular Disease Risk Factors

Measurement of Obesity Elevated FPG

High TG

Low HDL-C

Elevated BP

% % % %

Discussion

In our study we observed that BMI was more strongly associated with CVD risk in women and %BF and WHR were more strongly associated with CVD risk in men.

Epidemiologist use BMI as the best way to quantify obesity in their studies due to the practicality of risk assessment and application to public health concerns. However, these findings are significant if viewed from a clinical aspect due to the marginal associations.

There are various strength and weaknesses that affect the findings of the study. A major strength of this study lies in its validity and sampling scheme However since this sample was taken from a pool of Thai students and

75%). In women, BMI performed at least as well or better in predicting all CVD risk factors investigated in this study. The AUC for WHR were consistent lower than the other measures of adiposity (Figure 2).

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Elevated Fasting Plasma Glucose Elevated Blood Pressure

Among Men

Body mass index Tertile1 (<23.3) 8.8 14.3 4.8 32.0 (kg/m2) Tertile2 (23.3-25.7) 14.2 34.5 9.5 44.6 Tertile3 (>25.7) 19.7 46.9 25.2 64.4 Body fat percentage Tertile1 (<20.8) 4.7 12.2 2.7 33.3 (%) Tertile2 (20.8-24.2) 17.1 33.6 11.0 41.5 Tertile3 (>24.2) 21.7 50.0 25.7 66.0 Waist circumference Tertile1 (<81.0) 7.0 16.9 7.7 31.9 (cm) Tertile2 (81.0-87.9) 16.0 34.7 7.6 43.1 Tertile3 (≥88) 20.0 43.1 23.1 64.2 Waist to hip ratio Tertile1 (<0.85) 4.1 18.4 9.5 29.3 Tertile2 (0.85-0.89) 16.7 29.3 8.0 49.7 Tertile3 (>0.89) 22.8 48.3 22.1 62.2 Waist to height ratio Tertile1 (<0.48) 6.8 16.3 7.5 29.1 Tertile2 (0.48-0.51) 14.0 30.7 8.0 51.7 Tertile3 (≥0.52) 22.1 49.0 24.1 60.5

Among Women Body mass index Tertile (<21 5) 1 6 3 2 7 8 16 2 its validity and sampling scheme. However, since this sample was taken from a pool of Thai students and

professionals the research’s findings can not be generalized to the Thai population nor the general world. Moreover, this was a cross-sectional study so BMI and weight was measured at a single time point which may not reflect a true long-term weight of a subject thus affecting the results of this study.

Mortality from cardiovascular disease in Thailand has increased over the last few decades. The increase mortality appears to exceed those expected from aging of the population alone and many cases are attributable to obesity. With a better understanding of what forms of measuring obesity are best for each population, there can be more public and clinical awareness to prevent premature vascular disease.

This research was supported by Rachadapiseksompoj Faculty of Medicine Research Fund, Chulalongkorn University, Thailand and National Institutes of Health award (T37-MD001449), USA.

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0.2

0.0

Sen

sitiv

ity

1.00.80.60.40.20.0

1 - Specificity

0.6

0.4

0.2

0.0

Sen

sitiv

ity



Body mass index Tertile1 (<21.5) 1.6 3.2 7.8 16.2 (kg/m2) Tertile2 (21.5-24.7) 5.5 10.3 16.2 27.1 Tertile3 (>24.7) 16.0 20.6 25.7 47.4 Body fat percentage Tertile1 (<30.1) 2.3 2.9 6.6 17.4 (%) Tertile2 (30.1-35.1) 4.4 10.4 18.6 26.5 Tertile3 (>35.1) 16.1 21.2 24.4 46.6 Waist circumference Tertile1 (<70.0) 3.6 2.3 7.2 16.1 (cm) Tertile2 (70.0-77.9) 2.6 10.5 17.0 25.7 Tertile3 (≥78) 16.7 21.8 25.6 48.9 Waist to hip ratio Tertile1 (<0.75) 4.2 2.3 10.0 17.5 Tertile2 (0.75-0.80) 4.5 11.0 14.9 29.6 Tertile3 (>0.80) 14.2 21.7 25.2 43.7 Waist to height ratio Tertile1 (<0.44) 2.9 2.6 7.5 16.6 Tertile2 (0.44-0.49) 3.0 9.7 17.4 26.4 Tertile3 (≥0.50) 16.7 21.5 24.6 47.2

FPG = Fasting Plasma Glucose; TG = Triglyceride; HDL-C = High Density Lipoprotein; BP = Blood Pressure

The Relationship of Bottle Feeding and Other Sucking Behaviors with Speech Pathology in Patagonian Preschoolers

1Mary Parada, 2Clarita Barbosa, 2Sandra Vasquez, 2Juan Carlos Velez, 2 Chanaye Jackson,

Background R lt R lt C t

Mary Parada, Clarita Barbosa, Sandra Vasquez, Juan Carlos Velez, Chanaye Jackson,1David Yanez, 1Annette L. Fitzpatrick

1MIRT Program, University of Washington, Seattle, WA; 2El Centro de Rehabilitación, Punta Arenas, Chile

Background Results Results Cont.Delayed use of a bottle until after 9 months

protected children from subsequent speech pathologies by almost 70% (OR: 0.32, 95% CI: 0.10-0.98). A three-fold increased risk of speech pathology was found with use of a pacifier for more than three years (OR: 3.50, 95% CI: 1.11-11.03) and any finger-sucking behavior (OR: 3.04, 95% CI:1.15, 8.04). Results suggest that extended use of

Early reports in the literature suggest that sucking behaviors, including breast-feeding, use of bottles and pacifiers, may impact development of oral muscles and quality of speech in young children. We investigated associations between use of bottles, pacifiers, and other sucking behaviors with speech pathologies in children attending three preschools in Punta Arenas (Patagonia), Chile.

UnadjustedUnadjusted *Adjusted*AdjustedRisk FactorRisk Factor OR (95% CI)OR (95% CI) OR (95%IC)OR (95%IC)Age started bottle feeding

Less than 3 months Reference 1.0 Reference 1.0 3 to 9 months 0 65 (0 29 1 47) 0 66 (0 27 1 54)

Table 1. Results of logistic regression evaluating associations between speech pathology and sucking behaviors in children ages 3 to 5 in Punta Arenas,Chile 2006-2007

Objective

Methods

Conclusion

sucking outside of breast-feeding may have subsequent detrimental effects on speech development in young children.

The principal aim of this study was to evaluate associations between sucking habits, including breast and bottle feeding, with speech problems in children ages 3 to 5 years in Punta Arenas, Chile.

3 to 9 months 0.65 (0.29, 1.47) 0.66 (0.27, 1.54)More than 9 months 0.36 (0.12, 1.08) 0.32 (0.10, 0.98)

Bottle FedNo Reference 1.0 Reference 1.0Yes 0 .67 (0.14, 3.11) 0.68 (0.13, 3.46)

Time bottle feedingLess than 18 months Reference 1.0 Reference 1.018 to 36 months 0.73 (0.24, 0.22) 0.93 (0.29, 2.98)More than 36 months 1.12 (0.38, 3.23) 1.34 (0.44, 4.09)

These results suggest that sucking habits such as pacifier use, finger sucking and bottle feeding are indeed

i t d ith h W l h th t ith thMethodsInformation on infant feeding and sucking behaviors, including age at

starting and stopping breast- and bottle-feeding, pacifier use, and other sucking behaviors, was collected from self-administered questionnaires completed by parents. Evaluation of speech problems was conducted at the preschools with subsequent scoring by a licensed speech pathologist using age-normative standards. A total of 128 three- to five-year olds were assessed, 46% girls and 54% boys. The children were breast fed an average of 25.2 (SD 9.6) months and used a bottle 24.4 (SD 15.2) months. Fift th hild (41 7%) h d tl d ifi f

( , ) ( , )Breast fed

No Reference 1.0 Reference 1.0Yes 0.59 (0.09, 3.69) 0.71 (0.11, 4.57)

Time breast feedingLess than 6 months Reference 1.0 Reference 1.06 to 12 months 0.59 (0.25, 1.40) 0.61 (0.25, 1.48)More than 12 months 0.50 (0.20, 1.25) 0.51 (0.20, 1.32)

Use of pacifier

associated with speech. We can only hope that with these results better sucking habits will be encouraged to developing children.

References1. Neiva FC, Cattoni DM, Ramos JL, and Issler H. Early

Weaning: Implications to Oral Motor Development." J Pediatrir(Rio J). 2003; 79:7-12

Fifty-three children (41.7%) had or currently used a pacifier for an average of 11.4 (SD 17.3) months and 23 (18.3) were reported to have sucked their fingers.

Table 1. shows the results of a logistic regression of the test to evaluate speech processes. The results to this test were dichotomized into a binary score (Normal/Abnormal) based on the score attained by the child in comparison to other children within the same age range.

No Reference 1.0 Reference 1.0Yes 1.34 (0.66, 2.70) 1.48 (0 .71, 3.09)

Time with pacifierNo use Reference 1.0 Reference 1.0Less than 1 year 1.74 (0.50, 5.99) 1.90 (0.51, 7.14)1 to 3 years 0.58 (0.21, 1.59) 0.23 (0.23, 1.92)More than 3 years 3.23 (1.05, 9.99) 3.50 (1.11, 11.03)

Sucked Fingers Acknowledgements

2. Broad FE. The Effects of Infant Feeding on Speech Quality. New Zealand Med J .1972; 76 : 27-31.

3. Degan VV and Regina M P. Prevalence of Pacifier-Sucking Habits and Successful Methods to Eliminate Them-A Preliminary Study. Journal of Dentistry for Children. 2004; 72:148-151.

No Reference 1.0 Reference 1.0Yes 2.99 (1.14, 7.90) 2.81 (1.03, 7.71)

Time Sucking fingerDid not suck finger Reference 1.0 Reference 1.0Less than one year 0.96 (0.20, 4.51) 0.81 (0.15, 4.31)1 year to 30 months 5.13 (0.55, 47.4) 5.11 (0.51, 51.22)More than 30 months 5.13 (0.55, 47.4) 3.04 (1.15, 8.04)

AcknowledgementsThis research was supported in part by awards from

the National Institutes of Health, National Center on Minority Health and Health Disparities (T37-MD001449) and El Centro de Rehabilitaciones Club de Leones Cruz del Sur in Punta Arenas, Chile.

*Adjusted for age, gender and mother’s education

Depression in Caregivers of Children with Disabilities in Punta Arenas, ChileKrissett Alexis Loya*, Tess C. Lang*, Alisa Byquist*, Juan Carlos Velez**, Alejandro Yalinic**, Mauricio Diaz**, David Yanez*, Annette L. Fitzpatrick*

*MIRT Program, University of Washington, Seattle, WA; **El Centro Rehabilitacion Club de Leones Cruz del Sur, Punta Arenas, Chile

ResultsBackground ResultsD i i j bli h lth

Table 1. The Risk of Depression1 from caregivers with selected demographics in 142 children presenting at a public free rehabilitation clinic in Punta Arenas Chile 2006-2007 Odds ratios (OR) and 95% confidence intervals (95% CI) are shown for probable or demonstrating• Depression is a major public health concern

affecting almost 19 million members of the adult population.

• Factors such as income, education, gender, and marital status have been linked to depression. More recently, child rearing, children’s disabilities, and developmental disorders have been investigated with parental

Punta Arenas, Chile, 2006-2007. Odds ratios (OR) and 95% confidence intervals (95% CI) are shown for probable or demonstrating depression.Risk Factor Unadjusted

OR (95% CI)p Adjusted1

OR (95% CI)p

Sex of CaregiverMaleFemale

1.00 (reference).084 (.011-.653) .02

1.00 (reference).082 (.010-.640) .02

Age of Caregiver< 3535-45≥ 45

1.00 (reference)1.45 (.680-3.11)2.22 (.811-6.06)

.27

.34

.12

1.00 (reference)1.55 (.705-3.40)2.25 (.794-6.38)

.25

.28

.13

Num. of Children in Family1 1.00 (reference)

.611.00 (reference)

.93

The sample of caregivers ranged between ages 16 and 63 years with a mean age of 35.1 years (SD 8.9). The majority of interviewees were women (87%). Older caregivers were more likely to have more and older children (p <.001) as well as to have extended versus nuclear families (p=.01). Overall, 33.8% of caregivers were classified with probable depression and 14.1% demonstrated depression. There was a strong

Methods

ObjectiveTo identify risk factors for depression in caregivers of children with disabilities at a rehabilitation center in Punta Arenas (Patagonia), Chile.

g pdepression. 2

34 or more

( )1.38 (.551-3.42)1.46 (.568-3.73)2.36 (.669-8.36)

.50

.35

.22

( )1.07 (.403-2.86).865 (.293-2.55)1.27 (.328-4.93)

.89

.79

.73

Caregiver EducationCompleted or Some UniversityCompleted High School or Technical School Some High School or Technical SchoolCompleted Elementary SchoolLess than Elementary SchoolUnknown

1.00 (reference).

838 (.311-2.26)

1.04 (.326-3.30).778 (.180-3.36).889 (.201-3.93).889 (.201-3.93)

.99

.73

.95

.74

.88

.88

1.00 (reference)

.790 (.276-2.26)

.891 (.259-3.06)

.618 (.134-2.85)

.634 (.132-3.05)

.535 (.107-2.67)

.97

.66

.86

.54

.57

.45I 27 19

association between gender and depression with men being far less likely to be depressed (OR: 0.08, 95% CI: 0.01-0.64, p=.02). While total number of children in the family did not affect depression, parents with 2 or more children under age 5 years were more than four times as likely to be depressed as those with no younger children (OR: 4.51, 95% CI: 1.07-19.1). There was a borderline association with one’s dislike of the climate in the region (p=.08). Age, education of Methods Insurance

FONASA A2

FONASA,B,C,D3

PrivateMilitary

1.00 (reference).557 (.243-1.28).480 (.125-1.85)1.40 (.386-5.08)

.27

.17

.29

.61

1.00 (reference).532 (.221-1.28).446 (.109-1.83)1.77 (.417-7.52)

.19

.16

.26

.44

Number of Children ≤ Age 5None12 or more

1.00 (reference).489 (.228-1.05)2.97 (.890-9.91)

.006

.07

.08

1.00 (reference).598 (.226-1.58)4.51 (1.07-19.1)

.008

.30

.04

Sex of ChildFemaleMale

1.00 (reference).864 (.427-1.75) .68

1.00(reference).929 (.443-1.95) .85

During July 2006 and August 2007, 142 caregivers of children receiving therapy at El Centro de Rehabilitacion Club de Leones Cruz del Sur, were interviewed at the clinic with questions devoted to studying the level of depression. Information collected in the survey included demographics, family

caregiver, family income, and type of disability of the child were not related to depression. However, the caregiver’s perception of the care received at the clinic by the child significantly influenced his/her level of depression. Caregivers were more than four times as likely to be depressed if they were dissatisfied with the therapy (OR: 4.09 (1.09-15.36) or services received at the clinic (OR: 5.42, 95% CI: 1.50-19.62). Dissatisfaction with duration of therapy

Discussion

In an effort to understand the psychological impact a child with disabilities may have on his/her caregiver, we found

Like ClimateYesNo

1.00 (reference)1.84 (.872-3.86) .11

1.00 (reference)2.03 (.916-4.50) .08

Number Adults in Household2 or MoreSingle Parent

1.00 (reference).636 (.301-1.34) .64

1.00 (reference).530 (.234-1.20) .13

Type of FamilyNuclearExtensive

1.00 (reference).883 (.396-1.97) .76

1.00 (reference).744 (.307-1.80) .51

DisabilityNeurological Disorders 1.00 (reference)

.721.00 (reference)

.64

y g p ysituation, and perceptions of the care their child received. A pre-existing database, containing records of the caregivers and patients since 2000, provided additional information on social and other characteristics of the family, including the diagnosis of the disability of the child. Depression was assessed using DSM-IV

increased the risk of depression two-fold (OR: 2.19, 95% CI: 99-4.88).

with disabilities may have on his/her caregiver, we found that most socioeconomic indicators were not associated with depression. However, family stress produced by having several young children was related. Dissatisfaction with the therapy received by the child also increased the risk of depression. While theses cross-sectional results must be interpreted cautiously, this suggests that providers of therapy may have an opportunity to improve the mental health of caregivers by making sure care and treatment of

Cerebral PalsyPrematureMental RetardationDevelopmental DisabilitiesGenetic DisordersOther

1.10 (.366-3.31)2.13 (.393-11.6).862 (.256-2.89).720 (.236-2.20).229 (.024-2.15)1.07 (.346-3.28)

.87

.38

.81

.56

.20

.91

1.41 (.436-4.57)2.43 (.383-15.4).905 (.257-3.19).769 (.243-2.43).206 (.021-2.06)1.12 (.347-3.60)

.57

.35

.88

.66

.18

.85

Satisfaction with Duration of TherapySatisfiedNot Satisfied

1.00 (reference)2.30 (1.07-4.96) .03

1.00 (reference)2.19 (.986-4.88) .05

Satisfaction with Number of Apts. Available per WeekSatisfied 1.00 (reference) 1.00 (reference)

gcriteria. Probable and definite demonstrations of depression were categorized into a binary variable for analyses. Statistical analyses included multiple logistic regression to evaluate the risk of depression associated with factors collected in the interview. Unadjusted and adjusted models (for age and gender of the health of caregivers by making sure care and treatment of

their child is clearly understood.

Acknowledgement

This research was supported in part by awards from the National Institutes of Health, National Center on Minority Health and Health Disparities (T37-MD001449).

Thank you to my host family, Sandra and the Romero family for making my stay memorable and to the MIRT program.

[email protected]

1 Model is adjusted for age and gender of caregiver.2 FONASA A is government-sponsored health insurance provided free to very low-income participants at public hospitals. 3 FONASA B, C, D is government-sponsored health insurance provided with co-pays at public or private hospitals.

Not Satisfied( )

1.63 (.689-3.85) .27( )

1.71 (.682-4.30) .25

Satisfaction with the ServiceSatisfiedNot Satisfied

1.00 (reference)5.77 (1.73-19.2) .004

1.00 (reference)5.42 (1.50-19.62) .01

Satisfaction with the TherapistSatisfiedNot Satisfied

1.00 (reference)5.74 (1.11-29.6) .04

1.00 (reference)4.69 (.889-24.7) .07

Satisfaction with the TherapySatisfiedNot Satisfied

1.00 (reference)4.50 (1.31-15.4) .02

1.00 (reference)4.09 (1.09-15.36) .04

caregiver) were developed. Data were cleaned and analyzed using SPSS. A consent form was signed for each participant of the survey.

Anger Expression, Violent Behavior, and Symptoms of Depression Anger Expression, Violent Behavior, and Symptoms of Depression among Male College Studentsamong Male College Students

11Dale Terasaki, 11Bizu Gelaye, 2,32,3Yemane Berhane, MD, MPH,PhD 11Michelle A. Williams, Sc.D.1 1 Multidisciplinary International Research Training Program, University of Washington School of Public Health and Community Medicine, Seattle, Washington, USA

2 2 Addis Continental Institute of Public Health, Addis Ababa, ETHIOPIA3 Department of Community Medicine Addis Ababa University Addis Ababa ETHIOPIADepartment of Community Medicine, Addis Ababa University, Addis Ababa, ETHIOPIA

--Introduction Introduction ––Depression is an important global public health problem because of its high lifetime prevalence, and its association with poverty, malnutrition, and debilitating chronic disorders including angina,

Characteristics Depression* P-valueNo (N=899)n (%)

Yes (N=277)n (%)

College Education Level**Freshman 345 (38.5) 111 (40.4)Sophomore 179 (20 0) 48 (17 3)

Table 1: Selected Demographic and Lifestyle Characteristics According to Symptoms of Depression among College Students in Awassa, Ethiopia

Characteristics Depression Unadjusted OR (95% CI)

*AdjustedOR (95% CI)No (N=899)

n (%)Yes (N=277)n (%)

Anger Out Expression Score8-10 330 (36.7) 49 (17.7) 1.00 (Reference) 1.00 (Reference)11-14 348 (38 7) 105 (37 9) 2 03 (1 40-2 95) 1 97 (1 33-2 93)

Table 2: Odds Ratios (OR) and 95% Confidence Intervals (CI) of Depression among College Students in Awassa, Ethiopia

, g g g ,arthritis, diabetes, chronic headaches and migraines. Several investigators have documented associations between depression, violent behavior and outward anger expression.

To date, few studies have evaluated associations of violent behavior, anger coping styles, and symptoms of depression among African youths.

0.824Sophomore 179 (20.0) 48 (17.3)Junior 226 (25.2) 71 (25.6)Senior 146 (16.3) 45 (16.2)

ReligionOrthodox 489 (54.4) 144 (52.4)

0.194Protestant 268 (29.8) 88 (31.8)Muslim 72 ( 8.0) 15 ( 5.4)Other 60 ( 6.7) 28 (10.1)

Khat UseNo 613 (69.5) 176 (65.2)

0.104Yes 269 (30.5) 94 (34.8)

11 14 348 (38.7) 105 (37.9) 2.03 (1.40 2.95) 1.97 (1.33 2.93)≥15 221 (24.6) 123 (44.4) 3.75 (2.58-5.44) 3.23 (2.14-4.88)

Violent Act During Current YearNo 441 (49.1) 96 (34.7) 1.00 (Reference) 1.00 (Reference)Yes 458 (50.9) 181 (65.3) 1.82 (1.37-2.40) 1.12 (0.82-1.45)

Adjusted* OR (95% CI)

Separate models used for each variable*Adjusted for College Level (Continuous), Witnessing Parental Violence (Yes/No), Negative Life Events (Continuous), and Smoking (No/Yes)

Table 3: Adjusted ORs of Anger-Out Behaviors According to Depressive Symptoms among 1176 Male College Students in Awassa, Ethiopia

g y

--Objective Objective ––This cross-sectional study was conducted to assess associations of anger expression and violent behavior with symptoms of depression among male college students.

Smoking StatusNo 747 (88.0) 214 (81.4)

0.005Yes 102 (12.0) 49 (18.6)Alcohol Consumption

No 447 (52.5) 126 (48.1)0.121Yes 405 (47.5) 136 (51.9)

Witnessed Parental ViolenceNo 556 (75.0) 148 (65.5)

0.005Yes 185 (25.0) 78 (34.5)Negative Life Events

None 156 (17.4) 18 ( 6.5)

( )Anger-Out Behaviors1

Express anger 1.15 (0.84-1.59)

Make sarcastic remarks 1.43 (1.06-1.92)

Slam doors 1.88 (1.40-2.52)

Argue with others 1.63 (1.22-2.19)

Strike out 2.43 (1.78-3.31)

Say nasty things 2.06 (1.52-2.78)

*Adjusted for College Level (Continuous), Witnessing Parental Violence (Yes/No), Violent Behavior (No/Yes), Negative Life Events (Continuous), and Smoking (No/Yes)

-- Methods Methods --Participants were male undergraduate students from 17 departments in 9 colleges and universities in Awassa, Ethiopia.

A total of 1,176 students were included in the data analyses.

Information concerning outward anger expression violent

--Results Results ––Symptoms of depression was evident in 23.6% of participants

54 3% f d t t d itti t l t t f

--Discussion Discussion ––Our findings are consistent with an association between outward anger expression and symptoms of depression.

<0.001

1 98 (10.9) 20 ( 7.2)2 98 (10.9) 34 (12.3)3 112 (12.5) 47 (17.0)≥4 435 (48.4) 158 (57.0)

( )

Lose temper 1.78 (1.34-2.41)

Tell if someone annoys 1.01 (0.79-1.39)

Information concerning outward anger expression, violent behavior and symptoms of depression were collected using a self-administered questionnaire.

Logistic regression was used to estimate odds ratio (OR) with a 95% confidence interval (95% CI).

54.3% of respondents reported committing at least one act of violence in the current year, and

29.3% of respondents reported high (Spielberger Anger-Out score ≥15) levels of anger expression.

In multivariate analysis, moderate (adjusted OR=1.97; 95% CI 1 33 2 93) and high (adj sted OR 3 23 95% CI 2 14 4 88)

outward anger expression and symptoms of depression. Further research should be conducted to better characterize community and individual level determinants of anger, violent behavior and depression among African youths.

College students will likely influence the future economic, political, and socio-cultural fabric of their communities. School based inter ention programs designed to red ce1.33-2.93) and high (adjusted OR=3.23; 95% CI 2.14-4.88)

outward anger were statistically significantly associated with increased risks of depressive symptoms.

Violent behavior was associated with depressive symptoms (unadjusted OR=1.82; 95% CI 1.37-2.40).

AcknowledgementsAcknowledgements

This research was supported in part by an award from the National Institutes of Health, National Center on Minority Health and Health Disparities (T37-MD001449)

School-based intervention programs designed to reduce violent behavior and promote appropriate anger expression skills among adolescents and young adults are warranted.

Associations between Witnessing Parental Violence and Experiencing Symptoms of Depression among College Students

S Nicodimos1, B Gelaye1, MA Williams1, Y Berhane2,3

1Multidisciplinary International Research Training (MIRT) Program, University of Washington, Seattle, WA, 2Department of Community Health, Addis Ababa University, Addis Ababa, Ethiopia and

Gender based violence (GBV), any violence committed against women, is hidden, and is often regarded as a private family matter, or as a normal part of life. As a result, most children

Female Students N=217

Male StudentsN=305

Depressive symptoms questions during current *Adjusted OR **Adjusted OR

Female Witnessed Parental Violence

Male Witnessed Parental Violence

Yes (N=217) No (N=739) Yes (N=281) No (N=757)Characteristics n (%) n (%) n (%) n (%)

Age (yr)16-18 24 (11 1) 106 (14 4) *27 (8 9) 76 (9 3)

p y , y, , p3Addis Continental Institute of Public Health, Addis Ababa, Ethiopia

~Background & Objective~ Table 2. Adjusted odds ratio (OR) and 95% confidence intervals (CI) of depression and depressive symptoms in relation to history of witnessing parental violence, Awassa, Ethiopia, July, 2006

Table 1. Demographic characteristics of students according to their history of witnessing parental violence, Awassa, Ethiopia, July, 2007

, p ,grow up witnessing GBV in their homes and communities.

Studies have shown that witnessing GBV as a child is as associated with emotional and behavioral problems, including anxiety, depression, poor school performance, low self-esteem, mood swings, self-destructive behavior, alcohol dependency and physical health complaints

academic year (95% CI) (95% CI)Feeling bad about self

Never 1.00 (Reference) 1.00 (Reference)Several weeks during past year 1.89 (1.16 - 3.08) 1.31 (0.82 - 2.08)More than half of the past year 2.05 (1.19 - 3.53) 1.62 (0.93 - 2.82)Nearly the whole year 2.37 (1.26 - 4.44) 1.50 (0.88 - 2.57)

Trouble concentratingNever 1.00 (Reference) 1.00 (Reference)Several weeks during past year 1.36 (0.85 - 2.18) 1.23 (0.85 - 1.79)More than half of the past year 1.71 (1.01 - 2.90) 1.88 (1.18 – 3.00)Nearly the whole year 2.62 (1.66 - 4.14) 1.12 (0.72 - 1.74)

Thoughts of being better off deadNever 1.00 (Reference) 1.00 (Reference)

16 18 24 (11.1) 106 (14.4) 27 (8.9) 76 (9.3)19-20 105 (48.6) 347 (47.1) 89 (29.3) 318 (39.1)21-23 54 (25.0) 203 (27.5) 114 (37.5) 286 (35.1)>23 33 (15.3) 81 (11.0) 74 (24.3) 134 (16.5)

College Education LevelFreshman 92 (42.8) 318 (43.2) *107 (35.3) 332 (40.6)Sophomore 51 (23.7) 189 (25.7) 71 (23.4) 138 (16.9)Junior 58 (27.0) 182 (24.7) 85 (28.1) 200 (24.5)Senior 14 (6.5) 47 (6.4) 40 (13.2) 147 (18.0)

ReligionOrthodox 115 (53.2) 410 (55.9) 149 (49.2) 444 (55.0)Protestant 73 (33.8) 232 (31.7) 106 (35.0) 237 (29.3)Muslim 12 (5.6) 52 (7.1) 20 (6.6) 70 (8.7)Other 16 (7 4) 39 (5 3) 28 (9 2) 57 (7 1)

This cross-sectional study examines the association between witnessing parental violence in childhood and experience of depressive symptoms during the academic year among college students in Awassa, Ethiopia.

~Material and Methods~ Di i

( ) ( )Several weeks during past year 1.47 (0.92 - 2.36) 1.24 (0.79 - 1.95)More than half of the past year 2.53 (1.57 - 4.07) 1.97 (1.16 - 3.34)Nearly the whole year 1.60 (0.86 - 2.97) 1.93 (1.11 - 3.34)

*Adjusted for participants’ age (years), childhood residence (urban and rural), alcohol consumption (yes and no) and experience of gender-based violence during current year (yes, no). **Adjusted for participants’ age (years), childhood residence (urban and rural) and khat use (yes and no).

Other 16 (7.4) 39 (5.3) 28 (9.2) 57 (7.1)Childhood Residence

Urban *148 (69.6) 582 (79.2) *131 (43.1) 438 (54.3)Rural 66 (30.8) 153 (20.8) 173 (56.9) 368 (45.7)

Khat UserYes 30 (14.2) 88 (12.5) *109 (36.2) 226 (28.4)

SmokerYes 8 (3.8) 26 (3.8) 41 (14.3) 98 (12.6)

Alcohol ConsumerYes *54 (26.0) 132 (19.3) 149 (51.9) 366 (47.0)

Experienced GBV During Current Year

Yes *114 (54.5) 236 (34.7) -- --~Material and Methods~A total of 1,102 undergraduate students from colleges in Awassa, Ethiopia participated in the study.

A self-administered questionnaire was used to collect information on witnessing parental violence during childhood. Information concerning socio-demographic and lifestyle

Approximately 22.7% female students and 27.1% of the male students reported witnessing parental violence.

Females who witnessed parental violence were 3-times as likely to report moderately severe depression (OR=3.02; 95% CI 1.67-5.47) as compared with those who did not witness parental violence

~Results~Students who witnessed parental violence were more likely to report depressive symptoms, including diminished self esteem, suicidal thoughts.

Intervention programs focused on eradicating

~Discussion~

g g p ycharacteristics was also collected.

Depression and depressive symptoms were evaluated using a nine-item depression module of the Patient Health Questionnaire (PHQ-9).

Logistic regression procedures were used to estimate

as compared with those who did not witness parental violence.

Males who witnessed parental violence were 2.4-times (OR=2.42; 95% CI 1.41-4.13) more likely to report moderately severe depression.

Female students who witnessed parental violence were 2.4-times (OR=2.37; 95% CI 1.26-4.44) more likely to report feeling bad about themselves 2.6 times (OR=2.62; 95% CI 1.66-4.14) more likely to

Intervention programs focused on eradicating domestic violence must also address the needs of young adults from affected households. School-based counseling services may be one modality for addressing the needs of youths exposed to violence.

multivariable adjusted odds ratios (OR) and 95% confidence intervals (95% CI).

themselves 2.6 times (OR 2.62; 95% CI 1.66 4.14) more likely to have trouble in concentrating compared with those who did not witness parental violence.

Male students who witnessed parental violence were almost twice as likely to report having suicidal thoughts compared with their counterparts who did not witness parental violence (OR=1.97; 95% CI 1.16-3.34).

This research was supported in part by an award from the National Institutes of Health, National Center on Minority Health and Health Disparities (T37-MD001449) .

Perceptions and Attitudes of Gender Based Violence among College Students in Awassa, Ethiopia

N Bernal1, B Gelaye1, MA Williams1, Y Berhane2,31Multidisciplinar2Department of Community Health, Addis Ababa University, Addis Ababa, Ethiopia and

Gender-based violence (GBV) includes physical, sexual and psychological abuse from intimate partners or non-partners. GBV has been increasingly recognized as an important global

y International Research Training (MIRT) Program, University of Washington, Seattle, WA, 3Addis Continental Institute of Public Health, Addis Ababa, Ethiopia

Background & ObjectiveQuestions Surveyed Female Male

Total N= 1224n (%Agree)

Total N= 1313n (%Agree)

A woman should obey her boyfriend/husband 891 (77.3) 1069 (84.0)

Table 1- Views on gender and relationship among college students in Awassa, Ethiopia

Female Male

Total (N=1224) Total (N=1313) Agree Agree

n % n %

Table-2 Relationship between views supportive of male dominance and selected socio demographic and lifestyle characteristics

GBV has been increasingly recognized as an important global public health problem because of its acute and chronic effects on women’s health. In this cross-sectional study, we examined the attitudes and perceptions of GBV among college students in Ethiopia. We also investigated the relationships between certain socio-demographic and lifestyle characteristics to beliefs supportive of GBV.

A working woman should give her money to her husband 206 (17.8) 349 (28.1)

A man has the final say in matters 138 (11.5) 273 (21.2)

A man should share the housework 1082 (90.0) 1102 (85.4)

A woman needs her parents’ permission to work 169 (14.2) 282 (22.1)

A woman can do nothing if her husband takes girlfriends 54 (4.7) 65 (5.1)

If a wife/girlfriend does something wrong, her husband/boyfriend h i ht t i h h

274 (23.4) 341 (27.5)

ReligionOrthodox 119 53.6 567* 51.2Protestant 7 3.2 355 32.0Muslim 80 36.0 89 8.0Other 15 6.8 86 7.8

EducationFreshman 96 42.1 432* 39.0

Sophomore 63 27.6 216 19.5Junior 61 26.8 291 26.3Senior 8 3.5 167 15.1

Childhood residence

of GBV.

Material and MethodsThe study sample consisted of undergraduate students from colleges in Awassa, Ethiopia.

A total of 2537(1224 female and 1313 male) students

has a right to punish herIf a husband beats his wife, he is showing his love for her 81 (6.8) 1069 (84.0)

A married woman can refuse sex with her husband 339 (30.4) 258 (21.3)

It is always acceptable for a husband to hit his wife 41 (3.4) 52 (4.0)

It is always acceptable for a boyfriend to hit his girlfriend 50 (4.1) 61 (4.7)

It is sometimes acceptable for a husband to hit his wife 120 (10.1) 279 (22.1)

Rural 69* 30.4 568* 51.3Urban 158 69.6 531 47.9

Khat chewingYes 31 13.5 317* 28.6No 181 78.7 768 69.3

Tobacco smoking

Yes 8* 3.5 126* 11.4No 191 83.0 919 82.9

Alcohol consumption

Yes 51* 22.2 505 45.6N 149 64 8 546 49 3A total of 2537(1224 female and 1313 male) students

completed a self-administered questionnaire which collected information on socio-demographic characteristics, lifestyle habits childhood experiences, and perpetration of gender-based violence during the current academic year.

Approximately 18.6% of females and 84.2% of males were identified as “embracing” the male dominant point of view.

Results(cont.)

The results of our study indicate that most students agree with statements supportive of GBV.

Conclusions

It is sometimes acceptable for a boyfriend to hit his girlfriend 121 (10.1) 222 (17.8) * P-value (from Chi-Square test) < 0.05

No 149 64.8 546 49.3

R ltWomen who agree with male dominance statements were

more likely to have been raised in a rural setting and consume alcohol.

Among males, khat chewing and education level were statistically significantly associated with perceptions

ti f GBV

Students who agree with agree with male dominance statements were more likely to consume alcohol, chew khat and smoke cigarette.

Our findings emphasize the need for educational intervention programs that aim to increase awareness

Results6.8% of the female students agreed with a statement “if a

husband beats his wife, he is showing his love for her”, while 84.0% of the male students agreed with that statement.

Males were twice more likely to agree that it is sometimes supportive of GBV. about gender and relationship among college students

in Ethiopia.

This research was supported in part by awards from the National Institutes of Health, National Center on Minority Health and Health Disparities (T37-MD001449).

y gacceptable for a husband to hit his wife than females,

1.7-times more likely than females to agree that it is sometimes acceptable for a boyfriend to hit his girlfriend.

association between elevated liver enzymes and metabolic ...sensitivity c-reactive protein (crp, an...

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