Factors Associated with Perceived Mental Health Issues

The null hypothesis of no relationship between each socio-demographic and academic characteristic, and each type of perceived mental health issues was to be rejected if p-value of test statistic was less than 0.05. The following section describes the results of factors associated with perceived prevalence of mental health. This section is divided into two parts. The first part deals with results of bivariate analysis whereas the second part deals with results of multivariate analysis.

Results of simple binary logistic regression analysis: Table 4.10 shows the results of simple

binary logistic regression analysis with odd ratios at 95% Confidence Interval (CI). It was found that the factors predicting the likelihood of experiencing mental health issues were somewhat different across different types of mental health issues. However, sex was one of those factors which influenced all types of mental health issues. Male students in our study were less likely to perceive mental health issues in comparison to female students, i.e. PS (OR=0.74, 95% CI=0.58-0.94), DS (OR=0.75, 95% CI=0.58-0.96), and LWB (OR=0.70, 95% CI=0.53-0.93). The university had a bearing on the risk of perceived mental health issues. The students from BZU were 34% less likely to experience DS than the students from PU. Additionally, students enrolled in BA/BSC programs were at higher risk of DS than their master degree counterparts. Among BA/BSC students, those in second year of their study had a high risk of PS (OR=1.92, 95% CI=1.18-3.12) compared to students of third and fourth year at the same degree level.

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Table 4.10:

Factors associated with perceived prevalence of mental health issues vs. non prevalence of mental health (Simple binary logistic regression analysis employed separately with each type of mental health issue, N=1308)

123 Students living in hostel were 50% less likely to experience LWB than students living at their homes. Students who had sufficient financial support were less likely to have all types of mental health issues than their counterparts i.e. PS (OR=0.70, 95% CI=0.52-0.96), DS (OR=0.53, 95% CI=0.38-0.74), and LWB (OR=0.53, 95% CI=0.38-0.73). Since variation in age was quite narrow in our sample, age variable was dropped for all further analyses (Table 4.10).Monthly family income, employment status, academic faculty, type of degree, and place of birth were not significantly associated with any of the mental health issues. Similarly, age was not found to be associated with any of the dependent variables.

Results of multiple binary logistic regression analysis: Following the data driven approach, independent variables which showed significance at the earlier stage were entered in multiple binary logistic regression model with each type of mental health issues. Table 4.11 shows the results of factors associated with PS i.e. sex, year of study, current place of living, and sufficiency of financial support. As may be seen from the table, all the independent variables retained their significance despite slight variations in OR. Adjusted odd ratios (AOR) for the variables which were retained in the final model are presented in the result (Table 4.2).

Table 4.12 presents the probability of prevalence of DS when taken with a set of co-variants which were significantly associated in the simple binary logistic regression. Except marital status (AOR=0.53, 95% CI=0.28-1.01), all the other independent variables i.e. sex, university, degree program, and sufficiency of financial support retained their significance. AORs for all the variables did not change significantly, except that of university (AOR=0.71, 95% CI=0.52-0.98) which increased by 5%.

Table 4.13 demonstrates the results of multiple binary logistic regression analysis with the predictors of LWB which were significant in the simple binary logistic regression analysis. As per the results, university and year of study lost their significance. In contrast, sex, current place of living and sufficiency of financial support retained their significance in the model. Current place of living and sufficiency of financial support were stronger predictors of LWB than sex. Students living in university hostels and having highly sufficient financial support were 50% less likely to experience LWB than their counterparts. AOR for sex did not change significantly in the model (AOR=0.70, 95% CI=0.52-0.94). The detailed results based on the AORs for the variables retained in the final model are provided in the following tables.

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Table 4.11:

Impact of socio-demographic and academic characteristic on perceived stress (Multiple binary logistic regression model, N=1091)

Respondent characteristics

Perceived stress

Stressed (588) vs. Not-stressed (503) Adjusted odd ratios (95% CI)

Sex Male _{0.76 (0.59-0.97)*} Female 1.00 Year of study 2nd year 1.76 (1.07 - 2.88)* 3rd year 1.50 (.85 - 2.65) 4th year 1.00

Current place of living

University hostel 0.84 (.62 - 1.13)

Private hostel 0.67 (0.46 - 0.99)*

Home 1.00

Sufficiency of Financial Support

Fully sufficient 0.70 (0.51 - 0.95)*

Insufficient 1.00

Notes: 1= Reference category, OR=Odd ratios were adjusted for the other variables (respondent characteristics) retained in

the final model. ; CI= Confidence interval, *p < 0.05; **p < 0.01; ***p < 0.001

Table 4.12:

Impact of socio-demographic and academic characteristic on depressive symptoms (Multiple binary logistic regression model, N=979)

Respondent characteristics

Depressive symptoms

Depressed (432) vs. Not-depressed (547) Adjusted odd ratios (95% CI)

Sex Male 0.76 (0.59 - 0.99)* Female 1.00 Marital status Un-married 0.53 (0.28 - 1.01) Ever-married 1.00 University

Bahauddin Zakariya University 0.71 (0.52 - 0.98)*

University of Gujrat _{0.94 (0.69 - 1.30)}

University of the Punjab 1.00

Degree/ programme

B.A./ B.S. 1.33 (1.03 - 1.73)*

Master 1.00

Sufficiency of Financial Support

Fully sufficient 0.53 (0.38 - 0.74)***

Insufficient 1.00

Notes: 1= Reference category, OR=Odd ratios were adjusted for the other variables (respondent characteristics) retained in

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Table 4.13:

Impact of socio-demographic and academic characteristic on psychological well-being (Multiple binary logistic regression model, N=1256)

Notes: 1= Reference category

OR=Odd ratios were adjusted for the other variables (respondent characteristics) retained in the final model. *p < 0.05; **p < 0.01; ***p < 0.001; CI= Confidence interval