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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.)