Data was collected using Standard Valid Depression, Anxiety and Stress Scale (DASS-21) was used as the research instruments.
Ethical approval: Ethical clearance for the study was obtained from the Ethical Review Board of Nepal Health Research Council (Ref. No-1135/22 November, 202I), and written informed consent was obtained from each of the participants. The consent was documented from each respondent before the initiation of the response. Before data collection, all the participants were informed about the aims, objectives, and background of the study. Likewise, they were also informed regarding the risks and benefits of the study. Only those willing to participate and those providing informed consent were included in the study. Participants had the right to withdraw from the study at any point, if they so wish, without any negative repercussions. Confidentiality of the participant was maintained throughout the study and after data collection.
Study design: A quantitative analytical cross-sectional study was used to assess the anxiety, depression, and stress level among rural and urban female community health volunteers during the COVID-19 in Province No 1. Study Setting and Population: The setting of the study was all wards of selected rural and urban municipalities of Province 1. Province no. 1 is divided into 14 districts and then those districts are subdivided into Municipalities. There is one metropolitan city i.e. Biratnagar, two sub-metropolitan cities i.e. Itahari and Dharan. Altogether, there are 46 municipalities and 88 rural municipalities [14]. The total population of Province No. 1 is 4,534,943 among which total FCHVs is 51470 (including 47,328 FCHVs at rural/ Municipalities level 4142 at urban/Municipality) in Nepal [15]. The study population was recruited from all FCHVs of selected rural and urban municipalities of Province No 1. Study Population: The study population was all the FCHVs of one Metropolitan, one sub-metropolitan, and two rural municipalities (Biratnagar Metropolitan, Budiganga rural municipality of Morag district, and Itahari Sub metropolitan and Barju rural municipality of Sunsari district). The age of the study population was 20 to 60 years of FCHVs of Morang and Sunsari districts of Province No 1.
Sampling technique and sample size: For this study, a multistage random sampling technique was used for the selection of the sample. At the first stage, a total of two districts were selected by using a simple random technique among the 14 districts of Province No. 1. At the second stage, two municipalities (one urban and one rural) were selected from each district via a simple random sampling (lottery) method. However, the administrative and ethical clearance purpose of the NHRC, lottery method has been conducted among 14 districts of province 1 where Morang and Sunsari had been withdrawn. In Morang district altogether there are 17 municipalities (1 metropolitan, 8 urban municipalities, and 8 rural municipalities). Similarly in Sunsari district altogether there are 12 municipalities (two sub metropolitans’ four urban municipalities and six rural municipalities). Among 17 municipalities Biratnagar metropolitan as urban and Budiganga as the rural municipality had been selected by simple random method from the Morang district. Likewise, Sunsari district Itahari sub-municipality as urban and Barju as rural municipalities was selected by simple random (lottery method) technique among 12 municipalities from Sunsari districts. Total FCHVs were selected by the census method from these four municipalities.
Criteria for sample selection: first setting of the sample (Metropolitan, sub-metropolitan, and two rural municipalities) was selected with the bottleneck rotation method which is neck pointed direction was selected first then done accordingly. But the sample (FCHVs) was selected for the census method. Inclusion Criteria were those all-female community health volunteers present who were available at the time of data collection and were willing to participate in the study. Exclusion Criteria was the participants diagnosed with any kind of mental illness: depression, anxiety, and other psychological problems.
Sampling Frame
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)
Sample Size: The sample size was calculated based on Cochrane’s formula.
I.e. Sample size (n) = z2pq/d2
z = 1.96 at 95% Confidence Level
p = 42%= 0.42 (prevalence=41.9%~42%, obtained from the study on prevalence of mental health impacts among health workers during COVID-19 in a low resource setting: a cross-sectional survey from Nepal [16].
q = 1-p = 1- 0.42= 0.58
d = allowable error (5% = 0.05)
Then, after calculation, the sample size was 412 after adding 10% non-response but available total respondents were only 384 during data collection.
Instrumentation: A structured interview schedule was developed by researchers based on the objectives of the study. The study instrument was divided into three parts: Part 1: Socio-Demographic information of the participant Part 2: Questions related to interpersonal factors (social support system, social network, family, friendship, workgroup network) and organization factors (Policy-rules & regulation, Workplace, climate) of the participants. Part 3: Standard Valid Depression, Anxiety and Stress Scale (DASS 21). The instrument was translated in Nepali with the help of bilingual experts and as well as the back translation was done. Pretesting of the instrument was done among 10% (i.e. 41, where n=412) among FCHVs of Jhapa district for any clarity and modification in the research tool [17].
Table 2.1: The Recommended Cut-off Scores for Conventional Severity Levels (normal, moderate, severe) are as follows
Severity Level
|
Depression
|
Anxiety
|
Stress
|
Normal
|
0-9
|
0-7
|
0-14
|
Mild
|
10-13
|
8-9
|
15-18
|
Moderate
|
14-20
|
10-14
|
19-25
|
Severe
|
21-27
|
15-19
|
26-33
|
Extremely Severe
|
28+
|
20+
|
34+
|
Source: Lovibond, S. H. & Lovibond, P. F. (1995). Manual for the Depression Anxiety & Stress Scales. (2nd Ed.) Sydney: Psychology Foundation.
Note: Total Scores need to be multiplied by 2 to calculate the final score.
Validity and reliability of tool: The tool consists of three-part, part I consist of socio-demographical portion, part II consists of interpersonal factors of FCHVs, and part III consists of standard valid Depression, Anxiety and Stress Scale (DASS 21) was adopted which is developed by Lovibond & Lovibond. [48] It is used in various settings and various researchers in the world as well as Nepal too. The tool further consultation with research experts too for maintaining validity. The English version of the tool was translated into Nepali version by bilinguistic experts. The team researchers assess each item of the instruments along with all study variables for its relevancy by using the evaluation method via pretesting of the instruments and interview to measure the anxiety, depression, and stress level of the FCHVs. Each item of the tool measures the same theoretical construct asset objective of the study. The content validity was maintained by consultation with subject experts. For maintaining consistency, the principal researcher herself was engaged in data collection and monitoring, data entry, editing, and processing and analysis procedure. Data Collection Procedure: Data Collection Technique Data was collected via face-to-face interviews using a structured interview schedule for about one and half months (1st December to 14th January 2021). Each participant has explained the nature and purpose of the study. Safety precautions were maintained (physical distancing, using mask and sanitizer) before an interview. Informed consent (both verbal & written) was taken before performing the interview. The time duration for each interview was about 15-20 minutes. Collected data was checked for its completeness and editing was done on the same day to prevent recall bias, ensure the quality & accuracy of the study. Data Analysis Procedure: Collected data was checked daily for its completeness. All the data was kept for editing and coding. At first data entry was done in MS-excel followed by using the computer Statistical Package for the Social Sciences (SPSS) version 20.00. Descriptive analysis i.e. frequency, percentage, mean, median, and standard deviation was used to assess anxiety stress and depression level of the Female community health Volunteer of province one. Inferential analysis i.e. logistic regression was used to find the association between dependent and selected demographic variables p-value of ˃0.05 was considered to indicate statistical significance at a 95% confidence level. Analyzed data was presented in the relevant tabular form.
Table 1: Socio-demographic characteristics of the respondents
n=384
Variable
|
frequency (f)
|
Percentage (%)
|
Age
|
|
|
20 to 40 years
|
84
|
21.9
|
41 to 60 years
|
277
|
72.1
|
˃60
|
23
|
6.0
|
Mean±SD
|
47.51±9.28
|
|
Sex
|
|
|
Male
|
1
|
0.3
|
Female
|
383
|
99.7
|
Ethnicity
|
|
|
Brahmin/Chhetri
|
152
|
39.6
|
Janajati
|
34
|
8.9
|
Madeshi
|
174
|
45.3
|
Muslim
|
14
|
3.6
|
Dalit
|
10
|
2.6
|
Religion
|
|
|
Hindu
|
366
|
95.3
|
Buddhist
|
2
|
0.5
|
Muslim
|
14
|
3.6
|
Christian
|
2
|
0.5
|
Education
|
|
|
Literate only
|
106
|
27.6
|
Primary
|
48
|
12.5
|
Secondary
|
104
|
27.1
|
SLC
|
108
|
28.1
|
Bachelor and above
|
18
|
4.7
|
Occupation
|
|
|
FCHVs with Service
|
9
|
2.3
|
FCHVs with Business
|
10
|
2.6
|
only FCHVs
|
365
|
95.1
|
Type of Family
|
|
|
Nuclear
|
255
|
66.5
|
Joint
|
129
|
33.5
|
Marital status
|
|
|
Married
|
381
|
99.2
|
Unmarried
|
3
|
0.8
|
Personal habit
|
|
|
Relax
|
381
|
99.2
|
Tense
Place of Residence
Urban
Rural
|
3
200
184
|
0.8
58.1
47.9
|
Table 1 showed that nearly third-fourth (72.1%) of respondents were aged 40 to 60 years of age and 21.9% of respondents were aged 20 to 40 years. The mean age was 47.5 years whereas only 6.0% of respondents were aged above 60 years. Almost of the (99.7%) respondents were Female whereas only 0.3 % was male. Less than half (45.3%) of the respondents were Madhesi and more than one-third (39.6%) of respondents were Brahmin and Chhetri. Only 2.6% of Respondents were Dalit. Most of them (95.3%) of respondents were Hindu whereas 0.5% of respondents were Buddhist and Christian. More than one-fourth (28.1%) of the respondent’s education level was SLC whereas only 4.7% of respondents' education level was Bachelor and above. almost (95.1%) of the respondent's occupations were only FCHVs whereas only 2.3% of the respondents were FCHVs with did service. Two-thirds (66.4%) of the respondents had nuclear families whereas only 33.5% had a joint family. Similarly almost of the respondents were married whereas 0.8 % were unmarried.
Table 1 B: Socio-demographic Characteristics of Respondents
n=384
Variable
|
Frequency (f)
|
Percentage (%)
|
Personal habit
|
|
|
Relax
|
381
|
99.2
|
Tense
|
3
|
0.8
|
Social network
|
|
|
Use properly
|
371
|
96.6
|
Not use properly
|
13
|
3.4
|
Family support
|
|
|
Supportive
|
377
|
98.2
|
Not supportive
|
7
|
1.8
|
Workplace Climate
|
|
|
Favorable
|
380
|
99.0
|
Unfavorable
|
4
|
1.0
|
working environment
|
|
|
Supportive
|
373
|
97.1
|
Unsupportive
|
11
|
2.9
|
Policy
|
|
|
Favorable
|
381
|
99.2
|
Unfavorable
|
3
|
0.8
|
Table 2 revealed that almost (99.2%) of respondents were looked relaxed whereas only 0.8% of respondents were looked tense. Similarly, most of (96.6%) the respondents had a proper social network. Likewise almost (98.2%) of the respondents had supportive families whereas only 1.8% of the respondent’s families had unsupportive. Almost (99.0%) of the respondents had favorable working environments whereas only 2.9% of respondents had unfavorable working environments. Similarly almost (99.2 %) of respondents had expressed unfavorable policy for them whereas only 0.8% of respondents had expressed unfavorable policy for them.
Table 3: Respondent’s response on different variables of Depression Anxiety Stress
Variable
|
Did not apply to me at all (f) %
|
Apply to me some degree (f) %
|
Apply to me considerable degree (f) %
|
Apply to me very much (f) %
|
I found it hard to wind wound
|
147 (38.3)
|
197 (51.3)
|
21 (5.5)
|
19 (4.9)
|
It was aware of dryness of mouth
|
167 (43.5)
|
172 (44.8)
|
27 (7.0)
|
18 (4.7)
|
I couldn’t seem to experience any positive feeling at all
|
190 (49.5)
|
144 (37.5)
|
30 (7.8)
|
20 (5.2)
|
I experienced breathing difficulty
|
201(52.3)
|
138 (35.9)
|
29 (7.6)
|
16 (4.2)
|
I found it difficult to work up the initiative to do things
|
200 (52.1)
|
139 (36.2)
|
27 (7.0)
|
18 (4.7)
|
I tended to over-react to situations
|
199 (51.8)
|
138 (35.9)
|
27 (7.0)
|
20 (5.2)
|
I experienced trembling (e.g. in the hands)
|
207 (53.9)
|
133 (34.6)
|
28 (7.3)
|
16 (4.2)
|
I felt that I was using a lot of nervous energy
|
206 (53.6)
|
129 (33.6)
|
29 (7.6)
|
20(5.2)
|
I was worried about situations in which I might panic and make a fool of myself
|
201 (52.3)
|
132 (34.4)
|
34 (8.9)
|
17 (4.4)
|
I felt that I had nothing to look forward to
|
210 (54.7)
|
131 (34.1)
|
24 (6.3)
|
19 (4.9)
|
I found myself getting agitated
|
210 (54.7
|
132 (34.4)
|
23 (6.0)
|
19 (4.9)
|
I found it difficult to relax
|
218 (56.8)
|
122 (31.8)
|
25 (6.5)
|
19 (4.9)
|
I felt down-hearted and blue
|
208 (54.2)
|
129 (33.6)
|
28 (7.3)
|
19 (4.9)
|
I was intolerant of anything that kept me from getting on with what I was doing
|
210 (54.7)
|
122 (31.8)
|
32 (8.3)
|
20 (5.2)
|
I felt I was close to panic
|
213 (55.5)
|
123 (32.0)
|
33 (8.6)
|
15 (3.9)
|
I was unable to become enthusiastic about anything
|
213 (55.5)
|
125 (32.6)
|
30 (7.8)
|
16 (4.2)
|
I felt I wasn’t worth much as a person
|
220 (57.3)
|
121 (31.5)
|
26 (6.8)
|
17 (4.4)
|
I felt that I was rather touchy
|
210 (54.7)
|
126 (32.8)
|
31 (8.1)
|
17 (4.4)
|
I was aware of the action of my heart in the absence of physical exertion
|
219 (57.0)
|
121 (31.5)
|
26 (6.8)
|
18 (4.7)
|
I felt scared without any good reason
|
217 (56.5)
|
127 (33.1)
|
23 (6.0)
|
17 (4.4)
|
I felt that life was meaningless
|
218 (56.8)
|
123 (32.0)
|
24 (6.3)
|
19 (4.9)
|
Table 3 illustrated that in terms of, I found it hard to wind wound, more than half (51.3%) of respondents agree to apply to me some degree whereas more than one-third (38.3%) of respondents were agreed to apply to did not apply to me at all. in terms of, it was aware of dryness of mouth, less than half of respondents (44.8%) were agreed with applying to me some degree whereas (4.7%) of respondents were agreed with applying to me very much. Similarly in terms of, I couldn’t seem to experience any positive feeling at all, nearly half of the respondents (49.5%) agreed that did not apply to me at all whereas only (5.2%) of respondents agreed that apply to me very much. In terms of, I experienced breathing difficulty, more than (51.8%) of respondents agreed that did not apply to me at all whereas only 4.2% of respondents agreed with applying to me very much. In terms of, I found it difficult to work up the initiative to do things more than half (52.1%) of respondents agreed that did not apply to me at all-in terms of, I experienced trembling (e.g. in the hands) more than half (53.9%) of the respondents agreed that did not apply to me at all whereas only 7.3% of respondents were agreed with applying to me a considerable degree. In terms of, I felt that I was using a lot of nervous energy, more than half (53.6%) of the respondents agreed that did not apply to me at all whereas only 5.2% of respondents were agreed with applying to me very much. In terms of, I was worried about situations in which I might panic and make a fool of me, 52.3% of the respondents agreed that did not apply to me at all whereas only 4.4% were agreed with applying to me very much. In terms of, I felt that I had nothing to look forward to 54.7% of the respondents agreed that did not apply to me at all whereas only 4.9% were agreed with applying to me very much. In terms of, I found myself getting agitated, more than half (54.7%) of the respondents agreed that did not apply to me at all whereas only 4.9% were agreed with applying to me very much. In terms of, I found it difficult to relax, 56.8% of the respondents agreed that did not apply to me at all whereas only 6.5% were agreed with applying to me a considerable degree. In terms of, I felt down-hearted and blue, 54.2% of the respondents agreed that did not apply to me at all whereas only 7.3% were agreed with applying to me a considerable degree. In terms of, I was intolerant of anything that kept me from getting on with what I was doing, 54.7% of the respondents agreed that did not apply to me at all whereas only 8.3% were agreed with applying to me a considerable degree. In terms of, I felt I was close to panic, 55.5% of the respondents agreed that did not apply to me at all whereas only 8.6% were agreed with applying to me a considerable degree. In terms of, I was unable to become enthusiastic about anything, 55.5% of the respondents agreed that did not apply to me at all whereas only 7.8% were agreed with applying to me a considerable degree. In terms of, I felt I wasn’t worth much as a person, 57.3% of the respondents agreed that did not apply to me at all whereas only 6.8% were agreed with applying to me a considerable degree. In terms of, I felt that I was rather touchy, 54.7% of the respondents agreed that did not apply to me at all whereas only 8.1% were agreed with applying to me the considerable degree. In terms of, I was aware of the action of my heart in the absence of physical exertion, 57.0% of the respondents agreed that did not apply to me at all whereas only 6.8% were agreed with applying to me a considerable degree. In terms of, I felt scared without any good reason, 56.5% of the respondents agreed that did not apply to me at all whereas only 6.0% were agreed with applying to me a considerable degree. In terms of, I felt that life was meaningless, 56.8% of the respondents agreed that did not apply to me at all whereas only 6.3% were agreed with applying to me for a considerable degree.
Table 4: Prevalence of level of Depression Anxiety Stress of respondents
n=384
Variables
|
Frequency (f)
|
Percentage (%)
|
Level of Anxiety
|
|
|
Normal
Mild
|
194
8
|
50.5
2.1
|
Moderate anxiety
|
23
|
6.0
|
Severe anxiety
Extremely Severe
|
23
136
|
6.6
35.5
|
Level of Stress
|
|
|
Normal
Mild
|
226
18
|
58.9
4.7
|
Moderate Stress
|
90
|
23.4
|
Severe stress
Extremely Severe
|
6
44
|
1.6
11.4
|
Level of Depression
|
|
|
Normal
Mild
|
204
20
|
53.1
5.2
|
Moderate depression
|
29
|
7.6
|
Severe depression
Extremely Severe
|
83
48
|
21.6
12.5
|
Table 4 showed that half (50.5 %) of respondents had no anxiety and the prevalence of extremely severe levels of anxiety was 35.5 % of the respondent (FCHVs) whereas only 6.0% of respondents were moderate anxiety. Likewise, the prevalence of stress was 23.4% of moderate level of stress and 1.6 were the severe level of stress of respondents. Similarly, the prevalence of depression was 21.6% of respondents were a severe level of depression whereas only 7.6% of respondents had a moderate level of depression.
Table 5: Association between anxiety and selected demographic variables (Bivariate analysis)
n=384
Variable
|
Level of Anxiety
|
Unadjusted OR
|
CI
|
p-Value
|
|
Normal
|
Anxiety
|
|
|
|
Age
|
|
|
|
|
|
20 to 40 years
|
40
|
44
|
0.862
|
0.531-1.399
|
0.547
|
41 to 60 year
|
154
|
146
|
|
|
|
Ethnicity
|
|
|
|
|
|
Bramin/ Chhetri
|
85
|
67
|
0.699
|
0.463-1.054
|
0.087
|
Other castes
|
109
|
123
|
|
|
|
Religion
|
|
|
|
|
|
Hindu
|
|
|
1.022
|
0.397-2.633
|
0.964
|
Others
|
|
|
|
|
|
Occupation
|
|
|
|
|
|
Service & FCHVs
|
14
|
5
|
2.878
|
1.016-8.154
|
0.047 ⃰
|
Business & FCHVs
|
180
|
185
|
|
|
|
Types of Family
|
|
|
|
|
|
Nuclear
|
128
|
127
|
0.962
|
0.630-1.470
|
0.858
|
Joint
|
66
|
63
|
|
|
|
Marital Status
|
|
|
|
|
|
Married
|
193
|
188
|
0. .962
|
0. .630-1.470
|
0. 858
|
Unmarried
|
1
|
2
|
|
|
|
Social network
|
|
|
|
|
|
Good
|
187
|
184
|
2.595
|
0.497-13.540
|
0.258
|
Bad
|
7
|
6
|
|
|
|
Place of Residence
|
|
|
|
|
|
Urban
|
117
|
83
|
1.959
|
1.305-2.940
|
0.001 ⃰
|
Rural
|
77
|
107
|
|
|
|
Co-morbidities
|
|
|
|
|
|
Yes
|
145
|
137
|
1.020
|
0.956-1.088
|
0.559
|
No
|
49
|
53
|
|
|
|
Table 5 revealed that association between anxiety and selected demographic variable. There is significant association between anxiety and occupation of (p=0.047; OR=2.878; CI=1.016-8.154) and place of residence (p=0.001; OR=1.959; CI=1.305-2.940). The anxiety level is increased 2.878 times more in service with FCHVs then business with FCHVs and others. Likewise the FCHVs who lived in urban area are 1.959 times more chance to develop stress then FCHVs who lived in rural area. Similarly there is no association between age (p=0.547; OR=0.862; CI=0.531-1.399); ethnicity (p=0.087; OR=0.699; CI=0.463--1.054); religion (p=0.964; OR=0.1.022; CI=0.397-2.633) ; types of family (p=0.858; OR=0.962; CI=0.630-1.470); marital status (p=0.258; OR=2.595; CI=0.497-13.540); social network (p=0.559; OR=1.020; CI=0.956-1.088) and co-morbidities.
Table 6: Association between Stress and selected demographic variables (Bivariate analysis)
n=384
Variables
|
Level of Stress
|
Unadjusted OR
|
CI
|
p-Value
|
no stress
|
stress
|
Age
|
|
|
|
|
|
20 to 40 years
|
47
|
37
|
0.859
|
0.527-1.400
|
0.541
|
41 to 60 year
|
179
|
121
|
|
|
|
Ethnicity
|
|
|
|
|
|
Brahmin/Chhetri
|
97
|
55
|
1.408
|
0.925-2.143
|
0.110
|
Other castes
|
129
|
103
|
|
|
|
Religion
|
|
|
|
|
|
Hindu
|
217
|
149
|
1.456
|
0.565-3.755
|
0.437
|
Others
|
9
|
9
|
|
|
|
Education
|
|
|
|
|
|
below SLC
|
95
|
118
|
1.104
|
0.940-1.297
|
0.229
|
Above SLC
|
59
|
94
|
|
|
|
Occupation
|
|
|
|
|
|
Service & FCHVs
|
15
|
4
|
2.737
|
0.891-8.408
|
0.079
|
Business & FCHVs
|
211
|
154
|
|
|
|
Types of Family
|
|
|
|
|
|
Nuclear
|
149
|
106
|
0.949
|
0.617-1.461
|
0.813
|
Joint
|
77
|
52
|
|
|
|
Marital Status
|
|
|
|
|
|
Married
|
224
|
157
|
0.713
|
0.064-7.936
|
0.783
|
Unmarried
|
2
|
1
|
|
|
|
Social network
|
|
|
|
|
|
Good
|
220
|
157
|
0.891
|
.286-2.774
|
0.841
|
Bad
|
6
|
1
|
|
|
|
Place of Residents
|
|
|
|
|
|
Urban
|
117
|
83
|
1.630
|
1.082-2.454
|
0.019 ⃰
|
Rural
|
77
|
107
|
|
|
|
Co-morbidities
|
|
|
|
|
|
Yes
|
164
|
123
|
0.960
|
0.897-1.028
|
0.242
|
No
|
62
|
35
|
|
|
|
|
|
|
|
|
|
|
Table 6 revealed that association between stress and selected demographic variable. There is association between stress and place of residence (p=0.541; OR=0.859; CI=1.082-2.454). Likewise the FCHVs who lived in urban area are 1.630 times more chance to develop stress then FCHVs who lived in rural area. There is no significant association between stress and age (p=0.541; OR=0.859; CI=0.527-1.400); ethnicity (p=0.019; OR=1.408; CI=0.9252.143); religion (p=0.437; OR=0.1.456; CI=0.565-3.755); education (p=0.229; OR=1.630 ; CI= 0.940-1.297); occupation (p= 0.079; OR=2.737; CI= 0.891-8.408); types of family (p=0.813; OR=0.949; CI=0.617-1.461); marital status (p=0.783; OR=0.713; CI=0.0647-7.936); social network (p=0.841; OR=0.891; CI=0.286-2.774) and co-morbidities (p=0.242; OR=0.960; CI=0.897-1.028).
Table 7: Association between Depression and selected demographic variables (Bivariate analysis)
n=384
Variables
|
Level of Depression
|
Unadjusted OR
|
CI
|
p-Value
|
Non Depressive
|
Depressive
|
Age
|
|
|
|
|
|
20 to 40 years
|
44
|
40
|
963
|
0.593-1.563
|
0.877
|
41 to 60 year
|
160
|
140
|
|
|
|
Ethnicity
|
|
|
|
|
|
Brahmin/ Chhetra
|
62
|
90
|
1.503
|
1.503-2.272
|
0.054
|
Janajati & others
|
114
|
118
|
|
|
|
Religion
|
|
|
|
|
|
Hindu
|
195
|
171
|
1.140
|
0.443-2.938
|
0.786
|
Buddhist
|
9
|
9
|
|
|
|
Level of Education
|
|
|
|
|
|
Below SLC
|
194
|
172
|
.975
|
.768-1.237
|
.832
|
SLC and Above
|
10
|
8
|
|
|
|
Occupation
|
|
|
|
|
|
FCHVs with service
|
14
|
5
|
2.579
|
0.910-7.308
|
0.075
|
FCHVs with Business& other
|
190
|
175
|
|
|
|
Types of Family
|
|
|
|
|
|
Nuclear
|
136
|
119
|
1.025
|
.671-1.567
|
0.908
|
Joint
|
68
|
61
|
|
|
|
Place of Residents
|
|
|
|
|
|
Urban
|
121
|
79
|
1.864
|
1.242-2.797
|
0.003 ⃰
|
Rural
|
83
|
101
|
|
|
|
Status of Co-morbidities
|
|
|
|
|
|
Yes
|
152
|
135
|
0.996
|
0.933-1.064
|
0.912
|
No
|
52
|
45
|
|
|
|
Table 7 revealed that association between depression and selected demographic variable. There is association between depression and place of residence (p=0.003; OR=1.864; CI=1.242-2.797). Likewise the FCHVs who lived in urban area are 1.864 times more chance to develop depression then FCHVs who lived in rural area. There is no significant association between depression and age (p=0.877; OR=1.140; CI=0.593-1.563); ethnicity (p=0.832; OR=1.503; CI=1.503-2.272); religion (p=0.786; OR=0.1.456; CI=0.443-2.938); education (p=0.229; OR= 0.975; CI=0.768-1.237); occupation (p=0.075; OR=2.579; CI=0.910-7.308); types of family (p=0.908; OR=1.025; CI=0.671-1.567); and co-morbidities (p=0.912; OR=0.996; CI=0.933-1.064).
Part IV: Multivariate analysis between depression, anxiety stress, and socio-demographic variable of FCHVs
Table 8
Association between levels of Anxiety and Socio-demographic variables (Multivariate
Analysis)
n=384
Variables
|
Unadjusted OR
|
adjusted OR
|
CI
|
p-value
|
Ethnicity
|
|
|
|
|
Brahmin/ Chhetri
|
0.699
|
1.562
|
1.021-2.390
|
0.040 ⃰
|
Janajati & others ⁎⁎
|
(Ref)
|
|
|
|
Occupation
|
|
|
|
|
FCHVs with service
|
2.878
|
3.861
|
1.325-11.249
|
0.013 ⃰
|
FCHVs with businesses/others
|
(Ref)
|
|
|
|
Place of resident
|
|
|
|
|
Urban
|
1.959
|
1.959
|
1.470-3.413
|
≤0.001 ⃰
|
Rural
|
(Ref)
|
|
|
|
Table 8 depicts a multivariate analysis between the anxiety and demographic variable. There is significant association between level anxiety and ethnicity of FCHVs (p=0.040; OR=1.562; CI=1.021-2.390), occupation (p=0.013; OR=3.861; CI=1.325-11.249) and place of residence (p≤0.001; OR=1.959; CI=1.470-3.413). The FCHVs who are Brahmin/Chhetri is a 1.562 times more chance to develop anxiety than FCHVs who are Janajati and others. Similarly, FCHVs who did service is a 3.861 times more chance to develop anxiety than FCHVs did Business and others. Likewise, the FCHVs who lived in urban area are 1.959 times more chance to develop stress than FCHVs who lived in the rural area.
Table 9: Association between levels of Stress and Socio-demographic variables (Multivariate analysis)
n=384
Variables
|
Unadjusted OR
|
adjusted OR
|
CI
|
p-value
|
Occupation
|
|
|
|
|
FCHVs with service
|
1.864
|
3.363
|
1.074-10.531
|
0.037٭
|
FCHVs with Business
|
(Ref)
|
|
|
|
Place of resident
|
|
|
|
|
Urban
|
2.579
|
1.817
|
1.193-2.767
|
0.005٭
|
Rural
|
(Ref)
|
|
|
|
Table 9 depicts a multivariate analysis, there is significant association between level stress and occupation (p=0.037; OR=3.363; CI=1.074-10.531) place of residence (p=0. 005; OR=1.817; CI=1.193-2.767). FCHVs whose occupation was service is 3.363 times more chance to develop stress than the FCHVs who are FCHVs, business, other profession. Similarly, the FCHVs who lived in urban area are 1.817 times more chance to develop stress than FCHVs who lived in rural areas.
Table 10: Association between levels of Depression and Socio-demographic variables (Multivariate analysis)
n=384
Variables
|
Unadjusted OR
|
adjusted OR
|
CI
|
p-value
|
Ethnicity
|
|
|
|
|
Bramin/Chhetri
|
1.503
|
1.630
|
1.065-2.496
|
0.024 ⃰
|
Janajati & others
|
(Ref)
|
|
|
|
Occupation
|
|
|
|
|
FCHVs with service
|
2.579
|
3.371
|
1.158-9.812
|
0.026 ⃰
|
FCHVs with businesses& other
|
(Ref)
|
|
|
|
Place of resident
|
|
|
|
|
Urban
|
1.864
|
2.117
|
1.391-3.222
|
0.001 ⃰
|
Rural
|
(Ref)
|
|
|
|
* Significant association p-value < 0.05; ⁎⁎ Madhesi, Dalit & Muslin
Table 10 depicts a multivariate analysis between; there is significant association between level depression and ethnicity (p=0.024; OR=1.630; CI=1.065-2.496) occupation (p=0. 026; OR=3.371; CI=1.158-9.812) and place of residence (p≤0.001; OR=2.117; OR=1.391-3.222). FCHVs who are Brahmin/Chhetri; have 1.630 times more chance to develop depression than Janajati & others. FCHVs whose occupation is service have 3.371 times more chance to develop depression than Business, and others. Similarly, FCHVs who lived in urban is 2.117 times more chance to develop depression than who lived in rural respectively.