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Table 1 Socio-demographic characteristics of the participants in Arba Minch zuria district, 2019 (n = 807)

From: Maternity waiting homes as component of birth preparedness and complication readiness for rural women in hard-to-reach areas in Ethiopia

Variables

Categories

Frequency

Percent

Age category

15–24

40

5.0

24–35

506

62.7

35–48

261

32.3

Residency

Semi-Urban

73

9.1

Rural

734

90.9

Occupation

Housewife

693

85.9

Daily laborer

8

7.0

Farmer

37

32.5

Government employee

2

1.8

Private business

47

41.2

Merchant

15

13.2

Other

5

4.4

Ethnicity

Gamo

687

85.1

Wolayta

14

1.7

Zeyse

93

11.5

Othera

13

1.6

Religion

Orthodox

244

30.2

Protestant

546

67.7

Traditional

15

1.9

Jehovah's Witness

2

0.2

Marital Status

Married

804

99.6

Separated

2

0.3

Single

1

0.1

Educational statusb of the mothers

Illiterate

565

70.0

Read and write

3

0.4

Elementary & Primary

200

24.8

Secondary & preparatory

32

4.0

Above grade 12

7

0.9

Additional job than being housewife

No

693

85.8

Yes

114

14

Education status2 of the Husbands

(n = 804)

Illiterate

489

60.8

Read and write

10

1.2

Elementary & Primary

239

29.7

Secondary & preparatory

44

5.5

Above grade 12

22

2.7

Husbands’ occupation (n = 804)

Daily laborer

71

8.8

Farmer

637

79.2

Government employee

21

2.6

Merchant

36

4.5

Private

15

1.9

Otherc

24

3.0

Wealth quintile*

1st quantile

163

20.2

2nd quantile

160

19.8

3rd quantile

162

20.1

4th quantile

161

20.0

5th quantile

161

20.0

  1. a(2 Amhara, 7 Oromo, 1 Koyira 1 Konso, 1 Gurage, 1 Ganjule)
  2. b(Educational status: Elementary & Primary is from grade 1–8, and Secondary and preparatory is grade from 9–12)
  3. c(4 Pastor, 6 Jobless, 3 Driver, 4 Broker, 5 Student and 2 Retired)
  4. *(wealth quantile: wealth quantile has been constructed based on the principal component analysis approach. The questions were adopted from the Ethiopian Demographic Health Survey 2016 and included household ownership of assets, house and livestock, housing characteristics, and access to utilities and infrastructure, and then the household's possession of the assets or materials was coded into binary variables (No = 0/Yes = 1). Then households’ wealth quintiles (five equal groups) were constructed based on the weights for each asset from the 1st quintile (poorest) to 5th quantile (richest) in the study population.)