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Aim: To assess the direct and opportunity costs involved in utilising maternity waiting homes. Method: A cross-sectional admission survey administered to women who used ten maternity waiting homes across two rural districts in Zambia. A total of 3,796 women participated in the survey. Descriptive analysis was conducted on three domains of the data: demographic characteristics of women, direct costs, and opportunity costs. Findings: Waiting to deliver (86.3%), safe birth (70.8%), and distance (56.0%) were the most frequent reasons women reported for using a maternity waiting home. In terms of direct costs, roughly 65% of the women brought seven days or fewer days’ worth of food to the maternity waiting homes, with salt, mealie meals, and vegetables being the most frequently brought items. Only 5.8% of the women spent money on transport. More than half of the women reported paying user fees that ranged from 1 to 5 or more kwacha (US$0.10- 0.52). In terms of opportunity costs, 52% of the women participated in some form of income generating activities (IGAs) when at home. Approximately 35% of the women reported they lost earned income (1 to 50 or more kwacha) by staying at a maternity waiting home. Conclusion: A large proportion of women paid for food and user fees to access a maternity waiting home, while a low number of women paid for transport. Even though it is difficult to assign monetary value to women’s household chores, being away from these responsibilities and the potential loss of earned income appear to remain a cost to accessing maternity waiting homes. More research is needed to understand how to overcome these financial constraints and assist women in utilising a maternity waiting home.
A secondary analysis was conducted on cross-sectional admission surveys collected from women staying at ten MWHs in the Mansa, Chembe, and Lundazi districts in Zambia between 2016 and 2018. Because MWHs have existed in Zambia for decades with generally low quality and no standardised policy, the primary aim of the parent study was to implement MWHs using a MWH core model with specific standards and policy in relation to understanding the impact of standardised MWHs on reproductive health service access (Scott et al., 2018). Ten MWHs were implemented in a quasi-experimental parent study with the aim of evaluating the impact of the introduction of MWHs on reproductive health service access and maternal health outcomes. To collect robust data for decision-makers on the effectiveness of MWHs in Zambia, the parent study developed a core model for MWHs in rural Zambia with criteria in three domains: 1) infrastructure, equipment, and supplies, 2) policies, management and finances, and 3) linkages and services (Lori et al., 2018). In collaboration with the Ministry of Health, the ten sites in the Mansa, Chembe, and Lundazi districts were identified for the MWHs to be implemented. The detailed process of choosing the implementation site for the MWH and further details regarding the parent study are reported elsewhere (Lori et al., 2016; Scott et al., 2018). Data were collected daily via face-to-face interviews with women newly admitted to the MWHs between June 2016 and August 2018. At initial admission, the women were consented by local MWH caretakers, who are fluent in local languages. Women were informed they would be allowed to stay at the MWH regardless of their decision to participate in the interview. If the consent form was signed, the MWH caretakers proceeded with the interview by reading each of the questions in the survey and recording the answers. The surveys were then transcribed by the local research assistants into an Excel spreadsheet on a weekly basis. The detailed description of the collections tools development process is described elsewhere (Scott et al., 2018). Ethical approval was obtained from the Institutional Review Board (IRB) and the ERES Converge Research Ethics Committee of the authors’ institutions. Zambia consists of 10 provinces with 74 districts and a total population of 17.09 million (World Bank, 2019). It suffers from a high poverty rate: 58% of the population live below the international poverty line of US $1.9 per day (World Bank, 2018). The fertility rate is 4.7 births per woman, whereas women living in rural areas have two more children on average as compared to those living in urban areas (Central Statistical Office et al., 2018). The majority of births (80%) are assisted by skilled health care professionals; however, there is a difference between urban (93%) and rural (79%) areas (Central Statistical Office et al., 2018). Data were collected from women utilising ten MWHs in three districts, Mansa, Chembe, and Lundazi. The three districts were part of the Saving Mothers Giving Life (SMGL) initiative from 2012 to 2016 to reduce maternal and newborn mortality. This 5-year initiative was designed and implemented within the Global Health Initiative as a public-private partnership between the U.S government, Merck for Mothers, Every Mother Counts, the American College of Obstetricians and Gynecologists, the Norway government, and Project CURE (Kruck et al., 2016). Mansa has a population of 228,392 with 61.9% of the population living in rural areas and Lundazi a population of 323,870 with 95.1% of its population in rural areas (Central Statistical Office, 2010). The survey consisted of three separate domains: demographic characteristics, direct costs, and opportunity costs. The first domain contains of demographic questions that included age, gravida, parity, education level, number of companions (people who accompanied the women to MWH), types of companions (e.g. spouse/partner, mother, sister), and reasons for utilising the MWH. Women were allowed to choose more than one option for types of companion and the reason for coming to the MWH. The second domain collected information on both monetary and non-monetary direct costs involved in utilising MWHs. These questions include the amount of money spent on food, transport, user fees, and the woman’s willingness to return if they were required to pay user fees. Furthermore, questions such as “what food item did you bring from home?” and “how many days of food did you bring from home?” were asked. For the items of food, women were allowed to choose more than one option provided. Direct cost of a specific illness or disease is often defined as the cost involved in both in-patient and out- patient services, such as visits to healthcare professionals as well as other expenses associated with diagnosis and treatment (Anandarajah et al., 2016; Panopalis and Clarke, 2006; Slawksy et al., 2011). However, because reproductive health services have been provided for free in Zambia since 2006 and because we are concerned with the costs involved in MWH utilisation, we included informal costs for food, transport, and user fees under the category of direct cost. The last domain collected information about opportunity costs. This domain asked questions about the types of income generating activities (IGAs) the woman participated in, the type of activity in which the woman would be participating were she not at the MWH, and the amount of income she was losing by staying at the MWH (Keya et al., 2018). Opportunity costs were defined as the loss of potential gain from other alternatives when one alternative is chosen, such as the loss of income or opportunity due to inability to carry out specific activities (Anandarajah et al., 2016). It is much more challenging to accurately and comprehensively identify and calculate opportunity costs. Therefore, in this paper, we present two numerical figures for opportunity cost, one self-reported by the women and another calculated by multiplying the average monthly income per capita for rural households and the average number of days women stayed at the MWHs. Descriptive analysis was also conducted on the three domains of the data (demographic characteristics, direct costs, opportunity cost). The data were analysed using Stata 15.0 (StataCorp, College Station, TX, USA). The aim of this analysis was to 1) provide descriptive statistics for the demographic characteristics of the women, and 2) examine the direct costs and opportunity costs involved in utilising MWHs. Means and standard deviations (SD) were calculated to estimate the cost of food, transport, user fees, and lost income. Both conditional means, the average of those who paid anything for the specific category, and unconditional means, the average of those who paid anything and nothing for the specific category, were calculated. Frequency with percentages was calculated for categorical variables.
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