You are here

  • Home
  • Archive
  • Volume 15, Issue 3
  • Willingness to pay for maternity waiting home service among pregnant women in Simada district, Northwest Ethiopia: a facility-based cross-sectional study

Article Text

Article menu

Download PDFPDF

Health economics
Original research
Willingness to pay for maternity waiting home service among pregnant women in Simada district, Northwest Ethiopia: a facility-based cross-sectional study
  1. Temesgen Wodajnew1,
  2. Mezgebu Yitayal2,
  3. Nigusu Worku2,
  4. Asebe Hagos2
  1. 1Simada District Health Office, South Gondar Zone, Bahir Dar City, Ethiopia
  2. 2Department of Health Systems and Policy, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
  1. Correspondence to Dr Asebe Hagos; asebehagos21{at}gmail.com

Abstract

Objective The aim of this study was to determine the willingness to pay (WTP) for maternity waiting home (MWH) services and the associated factors among pregnant women in Simada district, Northwest Ethiopia.

Design Facility-based cross-sectional study design.

Setting The study was conducted in seven selected public health centres in Simada district, Northwest Ethiopia.

Participants A total of 423 pregnant women who attended antenatal care at selected public health centres were included as participants.

Outcome measures The outcome variable, WTP for MWHs, was estimated using the bid contingent valuation method. A Tobit regression model was used to examine the association between the predictors and the outcome variable.

Results A total of 423 pregnant women participated in this study, with a response rate of 97%. The majority, 86.6% (95% CI 80.20%, 92.42%) of participants were WTP for MWHs. The mean amount of money the pregnant mothers were WTP for MWH services per day was 24.35 ETB±16.85 (equivalent to US$0.76±0.53). The educational level (β= −0.181, 95% CI (−0.306 to −0.055) and wealth status (β=0.049, 95% CI (0.005 to 0.239) were factors significantly associated with WTP for MWH services.

Conclusion The vast majority of pregnant women were WTP for MWHs. However, the mean amount of money that the participants were WTP was less than the estimated and required amount of money for MWH expenses. Educational level and wealth status were identified as predictors of WTP for MWH services. Therefore, to establish a reliable and sustainable source of funding, it is desirable to introduce a supplemental financial strategy in addition to the community contribution.

  • HEALTH ECONOMICS
  • Health Equity
  • Public health
  • Health Services
  • Health economics

Data availability statement

Data are available upon reasonable request.

http://creativecommons.org/licenses/by-nc/4.0/

This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.

https://doi-org.ezproxy.u-pec.fr/10.1136/bmjopen-2024-089196

Statistics from Altmetric.com

    Request Permissions

    If you wish to reuse any or all of this article please use the link below which will take you to the Copyright Clearance Center’s RightsLink service. You will be able to get a quick price and instant permission to reuse the content in many different ways.

    Strengths and limitations of this study

    • The study aimed to estimate the willingness to pay (WTP) for maternity waiting home services, an area with limited evidence.

    • WTP studies may overestimate or underestimate the maximum amount individuals are WTP.

    • Including only mothers who attended antenatal care services may introduce sampling bias.

    • The study lacked qualitative support.

    Introduction

    Globally, the magnitude of maternal mortality is a major public health problem. In 2020, at a global level, 287 000 deaths occurred due to pregnancy and pregnancy-related complications. Nearly 70% of maternal deaths worldwide were attributed to sub-Saharan Africa.1

    According to the WHO’s estimation, the maternal mortality ratio in Ethiopia was 267 per 100 000 live births. In Ethiopia, 10 000 maternal deaths were reported, which accounted for approximately 3.6% of global maternal deaths in 2020. On the other hand, the 2019 Ethiopia Mini Demographic Health Survey reported that the perinatal and neonatal mortality rates were 29 and 33 per 1000 live births, respectively.1 2 The majority of pregnancy-related deaths in low- to middle-income countries are preventable by ensuring access to essential maternal health services such as antenatal care (ANC) and skilled birth attendants (SBA).3 In Ethiopia, evidence suggests that obstetric-related complications such as haemorrhage, obstructed labour, pregnancy-induced hypertension, puerperal sepsis and unsafe abortion are reported as the main causes of maternal deaths.4 5

    WHO recommended developing countries to incorporate maternity waiting homes (MWHs) as part of an integrated approach to reduce maternal morbidity and mortality.6 MWHs are residential facilities located near health facilities to accommodate pregnant mothers and await their delivery, providing easier access to maternal and newborn care. They facilitate the timely movement from home to health facilities, addressing various barriers like distance, geographical inaccessibility, seasonal challenges, infrastructure limitations, the means of transportation, the cost of transportation or communication between referral points.6–8

    Ethiopia has been implementing MWHs for nearly four decades. The first MWHs were established in the 1980s; however, their expansion to a broader geographic area and inclusion in health centres is a more recent development.9 MWHs are typically constructed within or near health facilities through the collaborative efforts of the government, local communities and non-governmental organisations. According to the Ethiopian national MWHs guideline, pregnant women living in remote areas, hard-to-reach areas for ambulance access, those at 38 weeks of pregnancy or beyond, those experiencing obstetric complications and those within 24 hours post partum are the primary candidates for admission to MWHs.10

    In 2016, the National Emergency Obstetric and Newborn Care assessment revealed that 53% of health facilities (56% of health centres and 27% of hospitals) had MWHs available for pregnant women.11 Despite their availability, the utilisation of MWHs by Ethiopian women shows significant variation, with reported rates ranging from as low as 7% to as high as 68%.12

    MWHs are an effective intervention in improving the coverage of maternal health indicators and reducing geographical inequalities in accessing maternal health service utilisations, including ANC, SBA, and immediate maternal and newborn postnatal care.9 They have made a significant contribution in reducing maternal and perinatal mortality.13–16 Studies demonstrated that maternal mortality among users of MWHs decreased by over 70%.17 Likewise, MWHs contribute to the reduction of more than two-thirds of stillbirths among users in Ethiopia.13 14 17

    Despite the promise shown by MWHs in improving maternal and perinatal outcomes, financial challenges and resource constraints exist in maintaining MWH services.9 15 Studies in Malawi and Zambia reported that lack of funds for food, shortage of materials and the unavailability of adequate and quality food were challenges for pregnant women to stay in MWHs and get service near the health facilities.18 19 Similarly, inadequate quantity and quality of food and poor hygiene practices were reported from MWH service users in Ethiopia.9 16

    In Ethiopia, the government fund did not allocate any budget to finance MWHs. The community’s voluntary contributions, in kind and cash, have been the major source of finance to run MWHs. Relying on such community contributions can lead to financial and resource shortages in public health centres and hospitals to provide quality service. Furthermore, concerns are raised about the sustainability of funds to finance MWH services.

    Looking for a reliable alternative financial approach is essential for maintaining the provision of MWH services. It is important to take into account the willingness of mothers to pay for MWH services as an alternative financial strategy to ensure the sustainability of the service. Willingness to pay (WTP) is defined as the maximum amount of money with an estimated amount the pregnant women can pay to have MWH service.20–22 As far as we know, the WTP for MWH service among pregnant women and associated factors have not been investigated in Ethiopia. Therefore, this study aimed to determine WTP for MWHs and identify factors among pregnant women in the Simada district, Northwest Ethiopia.

    Methods and materials

    Study design and setting

    A facility-based cross-sectional study design was conducted to assess WTP for MWHs and associated factors among pregnant women. The study was carried out in the Simada district, South Gondar Zone, Amhara National Regional State, Ethiopia, from 16 Februaryto 20 March 2020. Simada district is located 770 km from Addis Ababa, the capital city of Ethiopia. The total population of the district was projected to be 183 355 as predicted by the 2007 national census. The district has one primary hospital, seven health centres and two private clinics. All health centres have established MWHs. The district is divided administratively into two urban and 26 rural kebeles. Within a district, there are 256 healthcare professionals and 48 health extension workers who provide healthcare services for the communities.

    Source population and study population

    All pregnant women in the Simada district were the source population, whereas all pregnant women who attended ANC at a health centre during the study period in the Simada district were the study population for this study. Pregnant women who had ANC follow-ups and had already joined MWHs were excluded from the study for two reasons. First, since they received the service free of charge, they might perceive it as entirely cost-free, potentially introducing bias in responses to payment or cost-related questions. Second, the study aimed to assess hypothetical utilisation scenarios, whereas participants actively using MWHs were already engaged in the service, creating a methodological discrepancy between real-world experience and the study’s theoretical framework.

    Sample size determination and sampling technique

    The sample size was determined using a single population proportion formula, Embedded Image with an assumption of Z(a/2) at 95% confidence level (CL=1.96, the proportion of WTP (p=0.5), the tolerable margin of error (d=5%) and 10% non-response rate. Accordingly, the final calculated sample size was 423. According to the Simada health office report, 846 pregnant women had ANC visits per month in seven health centres.23 24 Based on average daily ANC visits, the K interval was calculated. Finally, a systematic random sampling technique was used. The first participant was randomly selected using the pregnant mother chart during the day of ANC visits; the next mothers were selected every second interval until the required sample size was reached.

    Study variables and measurement

    The outcome variable was WTP for MWHs, whereas sociodemographic and economic factors (age, residence, ethnicity, occupation, religion, educational status, marital status and wealth status), maternal health-related factors (history of using MWHs, history of pregnancy-related maternal complications, history of fetal complications and the presence of chronic disease) and geographic factors (distance and transportation) were included as predictor variables for the study. The household wealth index was developed based on various indicators associated with a household’s wealth status. These indicators included ownership of household items (eg, electricity, television, radio, watch, telephone and refrigerator), types of vehicles, access to water and sanitation facilities (eg, source of drinking water, type of toilet and sharing of toilet facilities), housing conditions (eg, materials used for the floor, walls and roof), ownership of agricultural land, and the type and number of animals owned.25 26 Principal component analysis was employed to construct the wealth index,25 26 which was subsequently divided into five categories or quintiles: quintile 1 (poorest), quintile 2, quintile 3, quintile 4 and quintile 5 (richest).

    Data collection tools and procedures

    An interviewer-administered semistructured questionnaire was developed by reviewing different literature (online supplemental file 1).27 28 The questionnaire was first prepared in English, then translated to Amharic (local language) and back to English to check its consistency. The questionnaire has three parts: the first part assesses the sociodemographic characteristics of the respondent. The second part of the questionnaire was about maternal health, MWHs and WTP of the study participants. The third part of the questionnaire was wealth index-related questions. The questionnaire was pretested in the Sediemuja district of South Gondar Zone among 21 pregnant women. Based on the findings, it was revised to enhance clarity and improve respondent comprehension. Six diploma-level clinical nurses for data collectors and one BSc nurse for supervisor were recruited. Prior to data collection, 1-day training was given for data collectors and the supervisor on data collection techniques, tools and the objectives of the study. During data collection, the whole procedure was supervised and checked by the principal investigator daily.

    Measurement of WTP for MWHs

    The WTP was measured by the bid contingent valuation method (CVM). CVM is a survey-based approach used to elicit consumers' monetary valuations of programme benefits for a cost–benefit analysis study.29 In CVM, first, the hypothetical market is described to respondents, and a series of questions are asked. Contingent valuation questions are used to estimate the demand function or the WTP distribution of consumers.30 31 For this study, a study scenario was proposed and defined for pregnant women as follows:

    “Maternal waiting homes are residential facilities located near health facilities where pregnant women can await their delivery. Using MWH services significantly reduces the maternal and fetal complications that may occur during delivery. While responding to the next questions, please keep the following points in mind:

    (1) The waiting home is safe and provides a space for you and your caretaker to sleep, and including basic cooking and sanitary facilities. (2) You will receive three meals per day along with your relatives. (3) Health care providers will offer appropriate and quality maternal health services to ensure you to deliver at the health facility. (4) Most pregnant women stay from 7 to 14 days, but you will not know exactly how many days you will be staying. You will need to pay a fee for each day you stay. (5) Your attendant or relatives stay in the MWH with you until you give birth.”

    Next, the pregnant women were asked a ‘yes’ or ‘no’ question about whether or not they would agree to pay for MWH services. The initial bid was 30 ETB (US$0.86), which was determined based on the current average cost of MWH services per night. The pregnant women were asked if they would pay the initial price. If the respondents said ‘yes’ to the initial bid, they were asked if they would pay the second higher bid, 40 ETB per night. If the respondents replied ‘no’ to the initial bid, they were asked if they would pay the second lower bid, 20 ETB per night. Then the question was repeated in a similar fashion, using a higher or lower bid value, until the participant’s maximum amount willing to pay was reached. The amount of money was reported in Ethiopian Birr and then converted into US$ (US$1 equivalent to 34.7857 ETB) based on the average annual exchange rate for 2020.

    Data management, processing and analysis

    The data was entered into EPI-INFO V.7 and exported to STATA V.14 for analysis. Descriptive statistics such as frequency, proportion, mean, SD, median and IQR were calculated and presented using graphs and tables. The Tobit model was used to analyse factors associated with WTP and the maximum amount of money that a pregnant mother was WTP. This model reveals both the probability of WTP and the maximum amount of money the respondents are WTP for MWH. It was measured by the bid contingent valuation method. The maximum amount of money the respondents might pay is given by:

    Embedded Image

    Where γ = outcome (MWTP for MWH); MWTP = Maximum WTP; xi = Explanatory variables; β0 = Slope; βi = Coefficient; ε = error term; 1 = Success/Yes; 0 =Failure/No

    The model estimates the marginal effect of an explanatory variable on the expected value of the dependent variable. To be free from serious data errors, the assumptions of the Tobit model, such as normality, linearity and multicollinearity were tested. A p value <0.05 was used to determine statistical significance.

    Patient and public involvement

    Patients and/or the public were not involved in the design, conduct, reporting or dissemination plans of this research.

    Results

    Sociodemographic characteristics of study participants

    In this study, 423 pregnant women were interviewed, and 410 of them responded, yielding a response rate of 97%. The mean age of the respondents was 31.61 (SD±6.25), which ranges from 18 to 44 years. About 88.54% of the study participants were married. More than half (54.63%) of respondents were unable to read and write. Among the respondents, 83.6% were housewives, 10.24% were government employees and 4.88% were merchants. Regarding the wealth status of the households, 20.98% were the poorest, whereas 18.29% were from the richest household wealth quintile. The majority (82.19%) of women travelled on foot to access health facilities (table 1).

    View this table:
    Table 1

    Sociodemographic characteristics of study participants in Simada district, Northwest Ethiopia, 2020 (n=410)

    Maternal health and MWH utilisation history

    The study showed that only one-fourth (24.63%) of the study participants had used MWHs previously. The study revealed that one in four (24.88%) mothers had a history of any pregnancy-related maternal complications, of whom 58.82% had bleeding. Additionally, about 24 (5.85%) pregnant mothers reported at least one type of chronic disease (cardiovascular disease, hypertension or diabetes mellitus) (table 2).

    View this table:
    Table 2

    MWH utilisation history and pregnancy-related problems among pregnant women in Simada district, Northwest Ethiopia, 2020 (n=410)

    WTP for MWH service

    The study reported that of the total number of respondents, 355 (86.59% (95% CI 80.20%, 92.42%)) of them were willing to pay for MWH services. The mean amount of money the pregnant mothers were WTP for MWH service per night was 24.35 ETB±16.85 (US$0.70±0.48). Nearly three-fourths (74.08%) of the participants were WTP below the estimated cost required for MWH services per night, 30 ETB (US$0.86). Moreover, the majority of pregnant women (58.59%) were WTP less than 20 ETB (US$0.57) per night. On the other hand, 11.55% and 15.21% of pregnant women were WTP in the range of 31–40 ETB (US$0.89–1.14) and >40 ETB (>US$1.14) per night, respectively, which was higher than the estimated cost of MWH services. The study also reported that 13.41% of the pregnant women were not WTP for MWH services. Lack of money was raised as a reason not WTP for MWH services (figure 1).

    Figure 1
    Figure 1

    The amount of money pregnant mothers are willing to pay for MWH services in Simada district, Northwest Ethiopia, 2020. ETB, Ethiopian Birr; MWH, maternity waiting home; WTP, willingness to pay.

    WTP for MWH service and associated factors

    The study identified the factors associated with WTP for MWH services and the maximum amount of money that a pregnant mother is WTP. In the Tobit regression analysis, educational level and wealth status were significantly associated variables with WTP for MWH services among pregnant women.

    Accordingly, the study reported that pregnant women who attend college education and above decreased their WTP for MWH services by 18 ETB compared with those unable to read and write (β= −0.181, 95% CI (−0.306 to –0.055).

    The study also revealed that pregnant women with the richest (quintile 5) households’ wealth status increased their WTP for MWHs service by 12 ETB compared with the poorest households’ wealth status (β=0.049, 95% CI (0.005, 0.239) (table 3).

    View this table:
    Table 3

    WTP for maternity waiting home and associated factors among pregnant women in Simada district, Northwest Ethiopia, 2020 (n=410)

    Discussion

    This study aimed to assess the WTP for MWH services and associated factors among pregnant women in Simada district, Northwest Ethiopia. The study reported that the vast majority (86.59%) of pregnant women were WTP for MWH services.

    The proportion of study participants’ WTP for MWHs was in line with a study conducted on WTP for maternal and child nutritional services in Ethiopia (88.8%)27 but higher than the proportion of WTP for cataract surgery (55%),32 insecticide-treated nets (68.5%)33 and podoconiosis interventions in Ethiopia (72.8%).34 The possible justification for variation might be because of the nature of health services. Differences in individual perceptions of diseases and medical conditions can be influenced by their respective consequences. Obstetric complications typically result in more severe outcomes compared with cataracts and podoconiosis.34 As a result, a larger number of women may be WTP for MWHs to minimise adverse birth outcomes. Additionally, people tend to be more responsive in terms of WTP for emergency and curative services than for preventive health measures, such as insecticide-treated nets.

    The remaining (13.42%) of pregnant women did not express WTP for MWHs. However, this does not imply that these women were unwilling to receive the MWH services. When asked the reason, they replied that lack of money was mentioned as a main barrier to not paying for MWH services.

    The mean amount pregnant women were WTP for MWH services per night was 24.35 ETB±16.85 (US$0.76±0.53), which is lower than the estimated required amount for each night’s stay. During the study period, 24 ETB could purchase two meals of injera beshiro, a common and affordable dietary food for low-income individuals in the study area. This amount is also lower than the WTP reported in a study conducted in Zambia.35 The differences may arise from variations in countries' health systems, the population’s economic status and knowledge about the importance of MWHs. Additionally, differences in study periods and the effects of inflation could also play a role.36 Moreover, differences in women’s expectations regarding the quality of MWH services might explain the observed variations between Ethiopia and Zambia.35

    Only one in four women was WTP the initial bid price. The majority of study participants were WTP below the initial bid of 30 ETB (US$0.86). Conversely, a small proportion of women were WTP more than the initial bid. The study highlighted that as pregnant mothers requested a higher amount of money (a higher price) per night, the proportion of participants’ WTP for MWH service decreased. This implies that a higher amount of payment can discourage pregnant women from using MWHs. In other words, women’s demand for MWH services can be decreased.

    The study also identified factors that influenced pregnant women’s WTP for MWHs. Maternal educational level and wealth status were significantly associated with the WTP for MWHs. The educational level had a negative association with WTP, whereas wealth status had a positive association with it.

    Pregnant women who attend college education and above decreased their WTP for MWH services as compared with those who were unable to read and write. This study was different from the study conducted in Bangladesh; higher educated people were willing to pay more for healthcare. The difference might be due to expectations and the types of services provided.30 The other possible justification might be that pregnant women with better educational status can reside in urban areas near health facilities and have easy access to health facilities when labour starts. Furthermore, some studies reported that the pregnant women in the MWH services faced inconveniences, inadequate and poor-quality foods, and received disrespectful service.16 18 19 Thus, these issues can affect educated pregnant women’s WTP for MWH services.

    The study also reported that households with a higher level of wealth status (quintile 5) increased their WTP for MWH service compared with the poorest quantile. It is in line with studies done in Gondar, Ethiopia.37

    In this study, most pregnant women did not accept the proposed amount (30 ETB, US$0.86), the current average cost of MWH services. To improve service quality, ensure financial adequacy and sustain MWH services, the government should consider alternative sustainable funding strategies. We recommended revising the funding strategy. In addition to the community contribution, it is important to consider other supplemental sources of funding to finance the MWH services.

    The study attempted to estimate the WTP for MWH services, and we believe that this is the first study in Ethiopia. However, the study has some limitations. The first limitation is that the study participants included in this study were mothers who attended ANC services, which may lead to sampling bias. The second limitation was that the present study was not supported by qualitative studies. Finally, due to the nature of WTP studies, the study may overestimate or underestimate during the determination of the maximum amount of WTP.

    Conclusion

    The study found that the majority of study participants were WTP for MWHs. However, the mean amount of money the participants were WTP for MWHs was lower than the estimated amount of money. Level of education and wealth status were factors associated with WTP for MWHs. A sufficient and sustainable source of funding is vital to improve and sustain the MWH services. Therefore, it is desirable to introduce a supplemental financial strategy in addition to the community contributions.

    Data availability statement

    Data are available upon reasonable request.

    Ethics statements

    Patient consent for publication

    Ethics approval

    Ethical clearance was obtained from the Ethical Review Committee of the Institute of Public Health, College of Medicine and Health Sciences, University of Gondar Comprehensive and Specialized Hospital with Ref No/IPH/837/06/2012 on the date 13 February 2020. A permission letter was obtained from the Simada District Health Office. Prior to data collection, informed verbal consent was obtained from each study participant. All participants participated in the study on a voluntary basis and were informed about their right not to participate in the study or withdraw at any time. The name and other identifying information were not recorded on the questionnaire and all information is kept strictly confidential.

    Acknowledgments

    Authors would like to thank the Institute of Public Health, College of Medicine and Health Sciences, University of Gondar. This manuscript is part of the master thesis work for the principal investigator. In addition, our gratitude goes to the Amhara National Regional State Health Bureau and the Simada District Health Office. Finally, we would like to thank the participants and data collectors for their contribution to the study.

    References

    1. ↵
      1. World Health Organization
      . Trends in maternal mortality 2000 to 2020: estimates by WHO, UNICEF, UNFPA, World Bank Group and UNDESA/Population Division: executive summary. 2023.
    2. ↵
      1. Ethiopian Public Health Institute (EPHI) [Ethiopia] and ICF
      . Ethiopia mini demographic and health survey 2019: final report. Rockville, Maryland, USA EPHI and ICF; 2021.
    3. ↵
      1. Yaya S,
      2. Ghose B
      . Global Inequality in Maternal Health Care Service Utilization: Implications for Sustainable Development Goals. Health Equity 2019;3:145–54. doi:10.1089/heq.2018.0082
      OpenUrlPubMed
    4. ↵
      1. Berhan Y,
      2. Berhan A
      . Causes of maternal mortality in Ethiopia: a significant decline in abortion related death. Ethiop J Health Sci 2014;24 Suppl:15–28. doi:10.4314/ejhs.v24i0.3s
      OpenUrlCrossRefPubMed
    5. ↵
      1. Mekonnen W,
      2. Gebremariam A
      . Causes of maternal death in Ethiopia between 1990 and 2016: systematic review with meta-analysis. Ethiopian Journal of Health Development 2018;32.
    6. ↵
      1. World Health Organization
      . Maternity waiting homes: a review of experiences. 1996.
    7. ↵
      1. World Health Organization
      . WHO recommendations on health promotion interventions for maternal and newborn health 2015. 2015,
    8. ↵
      1. van Braam EJ,
      2. McRae DN,
      3. Portela AG, et al
      . Stakeholders’ perspectives on the acceptability and feasibility of maternity waiting homes: a qualitative synthesis. Reprod Health 2023;20:101. doi:10.1186/s12978-023-01615-x
    9. ↵
      1. Activities, T.U.N.F.f.P
      . UNFPA supported maternity waiting homes in Ethiopia: good practices and lessons learned. 2018.
    10. ↵
      1. Ministry of Health, Ethiopia
      . Guideline for the establishment of standardized maternity waiting homes at health centers/ facilities. 2015.
    11. ↵
      1. Ethiopian Public Health Institute and Federal Ministry of Health
      . Ethopian Emergency Obstetric and Newborn Care (EmONC) assessment 2016: final report. 2017.
    12. ↵
      1. Kassa BG,
      2. Ayele AD,
      3. Belay HG, et al
      . Utilisation of maternity waiting homes and its associated factors in Ethiopia: Systematic reviews and meta-analysis. Clin Epidemiol Glob Health 2021;12:100891. doi:10.1016/j.cegh.2021.100891
      OpenUrl
    13. ↵
      1. Buser JM,
      2. Lori JR
      . Newborn Outcomes and Maternity Waiting Homes in Low and Middle-Income Countries: A Scoping Review. Matern Child Health J 2017;21:760–9. doi:10.1007/s10995-016-2162-2
      OpenUrlCrossRefPubMed
    14. ↵
      1. Legesse T,
      2. Abdulahi M,
      3. Dirar A
      . Trends and causes of maternal mortality in Jimma University Specialized Hospital, southwest Ethiopia: a matched case-control study. Int J Womens Health 2017;9:307–13. doi:10.2147/IJWH.S123455
      OpenUrlCrossRefPubMed
    15. ↵
      1. Tiruneh GT,
      2. Getu YN,
      3. Abdukie MA, et al
      . Distribution of maternity waiting homes and their correlation with perinatal mortality and direct obstetric complication rates in Ethiopia. BMC Pregnancy Childbirth 2019;19:1–11. doi:10.1186/s12884-019-2356-x
      OpenUrlCrossRefPubMed
    16. ↵
      1. Tiruneh GTT,
      2. Karim BW,
      3. Betemariam AM, et al
      . Maternity waiting homes in Rural Health Centers of Ethiop: The situation, women’s experiences and challenges. Ethiopian Journal of Health Development 2016;30:19–28.
      OpenUrl
    17. ↵
      1. Dadi TL,
      2. Bekele BB,
      3. Kasaye HK, et al
      . Role of maternity waiting homes in the reduction of maternal death and stillbirth in developing countries and its contribution for maternal death reduction in Ethiopia: a systematic review and meta-analysis. BMC Health Serv Res 2018;18:748. doi:10.1186/s12913-018-3559-y
      OpenUrlCrossRefPubMed
    18. ↵
      1. Suwedi-Kapesa LC,
      2. Nyondo-Mipando AL
      . Assessment of the quality of care in Maternity Waiting Homes (MWHs) in Mulanje District, Malawi. Malawi Med J 2018;30:103–10. doi:10.4314/mmj.v30i2.10
      OpenUrlPubMed
    19. ↵
      1. Sialubanje C,
      2. Massar K,
      3. Kirch EM, et al
      . Husbands’ experiences and perceptions regarding the use of maternity waiting homes in rural Zambia. Int J Gynecol Obstet 2016;133:108–11. doi:10.1016/j.ijgo.2015.08.023
      OpenUrlPubMed
    20. ↵
      1. Homburg C,
      2. Koschate N,
      3. Hoyer WD
      . Do Satisfied Customers Really Pay More? A Study of the Relationship between Customer Satisfaction and Willingness to Pay. J Mark 2005;69:84–96. doi:10.1509/jmkg.69.2.84.60760
      OpenUrl
    21. ↵
      1. Hayes DJ,
      2. Shogren JF,
      3. Shin SY, et al
      . Resolving differences in willingness to pay and willingness to accept. Am Econ Rev 1994;84:255–70. doi:10.2307/3146646
      OpenUrl
    22. ↵
      1. Wertenbroch K,
      2. Skiera B
      . Measuring Consumers’ Willingness to Pay at the Point of Purchase. Journal of Marketing Research 2002;39:228–41. doi:10.1509/jmkr.39.2.228.19086
      OpenUrlCrossRefWeb of Science
    23. ↵
      1. Simada Woreda Health Office
      . 2018 annual performance report and 2019 annual plan [unpublished report]. Simada Simada Woreda Health Office; 2018.
    24. ↵
      1. Simada Woreda Health Office
      . 2019 annual performance report [unpublished report]. Simada Simada Woreda Health Office; 2019.
    25. ↵
      1. Rogers DW,
      2. Alexander GRW,
      3. Pozzi N, et al
      . Wealth index mapping in the horn of Africa. FAO animal production and health working paper 4. 2011.
    26. ↵
      1. Rutstein SO
      . Steps to constructing the new DHS wealth index. Rockville, MD ICF International; 2015.6.
    27. ↵
      1. Chakraborty NM,
      2. Fry K,
      3. Behl R, et al
      . Simplified Asset Indices to Measure Wealth and Equity in Health Programs: A Reliability and Validity Analysis Using Survey Data From 16 Countries. Glob Health Sci Pract 2016;4:141–54. doi:10.9745/GHSP-D-15-00384
      OpenUrlAbstract/FREE Full Text
    28. ↵
      1. EDHS/CSA
      . Ethiopia demographic and health survey. ICF International, Central Statistical Agency; 2016.
    29. ↵
      1. Klose T
      . The contingent valuation method in health care. Health Policy 1999;47:97–123. doi:10.1016/s0168-8510(99)00010-x
      OpenUrlCrossRefPubMedWeb of Science
    30. ↵
      1. Pavel MS,
      2. Chakrabarty S,
      3. Gow J
      . Assessing willingness to pay for health care quality improvements. BMC Health Serv Res 2015;15:43. doi:10.1186/s12913-015-0678-6
    31. ↵
      1. Venkatachalam L
      . The contingent valuation method: a review. Environ Impact Assess Rev 2004;24:89–124. doi:10.1016/S0195-9255(03)00138-0
      OpenUrl
    32. ↵
      1. Seid M,
      2. Minyihun A,
      3. Tilahun G, et al
      . Willingness to pay for cataract surgery and associated factors among cataract patients in Outreach Site, North West Ethiopia. PLoS ONE 2021;16:e0248618. doi:10.1371/journal.pone.0248618
    33. ↵
      1. Aleme A,
      2. Girma E,
      3. Fentahun N
      . Willingness to pay for insecticide-treated nets in Berehet District, Amhara Region, Northern Ethiopia: implication of social marketing. Ethiop J Health Sci 2014;24:75–84. doi:10.4314/ejhs.v24i1.10
      OpenUrlPubMed
    34. ↵
      1. Tamiru A,
      2. Tsegay G,
      3. Wubie M, et al
      . Podoconiosis patients’ willingness to pay for treatment services in Northwest Ethiopia: potential for cost recovery. BMC Public Health 2014;14:1–8. doi:10.1186/1471-2458-14-259
      OpenUrlCrossRefPubMed
    35. ↵
      1. Vian T,
      2. White EE,
      3. Biemba G, et al
      . Willingness to Pay for a Maternity Waiting Home Stay in Zambia. J Midwife Womens Health 2017;62:155–62. doi:10.1111/jmwh.12528
      OpenUrl
    36. ↵
      1. Belete GT,
      2. Walle Y
      . Willingness to pay for medical care and its determinants in private health care facilities among Gondar city residents, Northwest Ethiopia: Cross sectional study. Heliyon 2023;9:e21143. doi:10.1016/j.heliyon.2023.e21143
    37. ↵
      1. Abiye S,
      2. Yitayal M,
      3. Abere G, et al
      . Health professionals’ acceptance and willingness to pay for hepatitis B virus vaccination in Gondar City Administration governmental health institutions, Northwest Ethiopia. BMC Health Serv Res 2019;19:796. doi:10.1186/s12913-019-4671-3
      OpenUrlPubMed

    Footnotes

    • Contributors TW: conceptualisation, data curation, formal analysis, investigation, methodology, project administration, resources, software, validation, visualisation, writing original draft and writing—review and editing. MY: methodology, formal analysis, investigation, supervision, writing—review and editing. NW: methodology, software, visualisation, writing—review and editing. AH: data curation, formal analysis, investigation, methodology, software, supervision, writing original draft and writing—review and editing. AH acted as guarantor.

    • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

    • Competing interests None declared.

    • Patient and public involvement Patients and/or the public were not involved in the design or conduct or reporting or dissemination plans of this research.

    • Provenance and peer review Not commissioned; externally peer reviewed.

    • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.