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Factors Influencing Women’s Choice Of Place Of Delivery In Nigeria

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Maternal mortality in Kenya increased from 380/100000 live births to 530/100000 live births between 1990 and 2008. Skilled assistance during childbirth is central to reducing maternal mortality yet the proportion of deliveries taking place in health facilities where such assistance can reliably be provided has remained below 50% since the early 1990s. We use the 2008/2009 Kenya Demographic and Health Survey data to describe the factors that determine where women deliver in Kenya and to explore reasons given for home delivery.


Data on place of delivery, reasons for home delivery, and a range of potential explanatory factors were collected by interviewer-led questionnaire on 3977 women and augmented with distance from the nearest health facility estimated using health facility Global Positioning System (GPS) co-ordinates. Predictors of whether the woman’s most recent delivery was in a health facility were explored in an exploratory risk factor analysis using multiple logistic regression. The main reasons given by the woman for home delivery were also examined.


Living in urban areas, being wealthy, more educated, using antenatal care services optimally and lower parity strongly predicted where women delivered, and so did region, ethnicity, and type of facilities used. Wealth and rural/urban residence were independently related. The effect of distance from a health facility was not significant after controlling for other variables. Women most commonly cited distance and/or lack of transport as reasons for not delivering in a health facility but over 60% gave other reasons including 20.5% who considered health facility delivery unnecessary, 18% who cited abrupt delivery as the main reason and 11% who cited high cost.


Physical access to health facilities through distance and/or lack of transport, and economic considerations are important barriers for women to delivering in a health facility in Kenya. Some women do not perceive a need to deliver in a health facility and may value health facility delivery less with subsequent deliveries. Access to appropriate transport for mothers in labour and improving the experiences and outcomes for mothers using health facilities at childbirth augmented by health education may increase uptake of health facility delivery in Kenya.


Approximately 1000 women die each day worldwide from pregnancy related causes, 99% of them in developing countries and more than 50% in sub-Saharan Africa [1] with most deaths concentrated around the time of delivery. An estimated 2.65 million stillbirths occurred in 2008 worldwide [2] while 3 million new-borns do not survive the first month of life worldwide annually [3]. Skilled assistance during childbirth, readily accessible appropriate care in case of complications and effective postnatal care within the first 24 hours of delivery are strategies that can improve perinatal outcomes for mothers and babies [456]. A key strategy to reducing maternal and neonatal deaths is the ‘health-centre intrapartum care strategy’, where qualified skilled workers manage labour, effectively manage complications and are supported with effective referral systems for specialised care when needed, and an effective postnatal care package [47].

A significant proportion of mothers in developing countries still deliver at home unattended by skilled health workers [58]. In diverse contexts, individual factors including maternal age, parity, education and marital status, household factors including family size, household wealth, and community factors including socioeconomic status, community health infrastructure, region, rural/urban residence, available health facilities, and distance to health facilities determine place of delivery and these factors interact in diverse ways in each context to determine place of delivery [91011]. Eijk et al. looked at antenatal care and delivery care among women in Western Kenya and demonstrated that older women, high parity, lower socioeconomic status, low education levels and more than an hour walking distance were associated with delivery outside health facilities[12]. Studying poor urban dwellers in Nairobi, Fosto et al. found from bivariate analyses that wealth, education, parity, place of residence were associated with place of delivery [13]. Ochako has previously demonstrated that these factors together with marital status and age at birth of last child determined use and timing of first Antenatal Care (ANC) visit and type of delivery [14]. There are also wide variations in the reasons women give for delivering at home between and within countries [8151617]. For Kenya, recent studies looking at the degree of effect of such factors are lacking.

In Kenya, maternal mortality rate has not reduced over recent years, and may even have increased from an estimated 380/100000 live births in 1990 to 530/100000 live births in 2008 [1]. Although a number of factors may have contributed to this, including improved identification of maternal deaths, health facility delivery remained low at 44% and 42.6% in the early 1990s and in 2008 respectively [1819]. Recent evidence on determinants of place of delivery in Kenyan utilising a nationally representative data and controlling for all factors is lacking, yet understanding the influences on place of delivery in Kenya is crucial to identifying key priority areas for policy and practise to increase the prevalence of skilled assisted deliveries.

We have used data from the 2008/2009 Kenya Demographic and Health Survey (KDHS) and linked them with a 2008 Kenyan Health Facility Database, that provides Global Positioning System (GPS) coordinates for distance analysis, to describe the factors that influence where women deliver in Kenya, and the reasons that women give for delivering at home.

Study population

The 2008/2009 KDHS is a nationally representative household-based survey, with interviewer administered questionnaires used to obtain a range of detailed health related and demographic information, and focussing on maternal and child health. Using the 1999 Kenya Population and Housing Census, a two-stage cluster sampling technique was used to sample 10000 households from 400 clusters and 8444 women aged 15–49 years and men age 15–54 years were interviewed. Details of the survey, sampling approach, including the questionnaires used, have been reported elsewhere [19]. In this study, after a description of all deliveries within the five years preceding the survey, we base the rest of the analysis on data for the most recent delivery for each mother.

The KDHS data collection procedures were approved by the ICF Macro (Calverton, Maryland), Institutional Review Board and the Scientific and Ethical Review Committee of the Kenya Medical Research Institute (KEMRI) and informed consent was obtained from respondents at the start of the individual interviews [19]. Permission to use these data was obtained from ‘Measure DHS’ [20]. No further ethical approval was necessary since the study was based on anonymous public use data with no identifiable information on survey respondents.

Outcome and explanatory variables

Women were asked about “place of delivery” and whether this was “at a health facility”, “at home” or “en route to a healthcare provider”. The latter two responses were combined together for this analysis given that the latter group was small (1.14% (n=45)) to be analysed separately and reasoned that this may reflect women who attempt to deliver at home and only decide to go to a health facility much later. A subsidiary question asked for the “main reason for home delivery” with women selecting their main reason from the following list of ten options: facility too far/no transport, not necessary, abrupt delivery, cost too much, facility not open, don’t trust facility, not customary, family did not allow, no female provider, and other (unspecified).

From the questionnaire data available, we selected to analyse 16 explanatory variables which, based on a review of literature, have potential to influence place of delivery: maternal age, education, parity, marital status, number of ANC visits, healthcare provider at ANC, health facility of ANC, insurance, household size, relationship to household head, wealth index, presence of co-wife, rural/urban residence, ethnic group, region of residence and religion. These were classified for analysis under four broad themes: (1) socio-cultural factors, (2) perceived benefit/need of skilled attendance (3) physical accessibility, and (4) economic accessibility in a framework adapted by Gabrysch et al. (2009) from the Thaddeus and Maine’s three delays model (delay in decision to seek care, in reaching care and in receiving care) of delivery care use [21].

The wealth index, a proxy measure of a household’s long-term standard of living, is based on consumer goods, dwelling characteristics, type of drinking water source, toilet facilities, among others. Details of the philosophy and construction of the indices are discussed in detail by Measure DHS [22].

Maternal ages at delivery were computed from the mothers’ and babies’ birthdates. The distance of each household from the nearest health facility was calculated using GPS coordinates for households from the KDHS and for health facilities from the 2008 Kenya Health Facility Database obtained from Malaria Atlas Project (MAP) and developed by the Kenya Medical Research Institute (Kemri)-University of Oxford-Welcome Trust Collaborative Programme [23]. The Kenya Essential Package for Health as contained in The Second National Health Sector Strategic Plan of Kenya (NHSSP II), documents that all health facilities from level 2 dispensaries and clinics provided delivery services supervised by skilled health staff in 2004 [24] and therefore all health facilities contained in the health facility database are presumed to serve as a first point of contact in the healthcare system for a woman in labour. The household GPS coordinates were slightly displaced for each household after the survey to within 0-5 km in rural areas, 0-2 km in urban areas and 0-10 km in 1% of sparsely populated areas of Kenya to maintain confidentiality for respondents [25].

Statistical methods

The bivariate associations between each potential risk factor and delivery at a health facility were explored, and those significant at p<0.05 were entered together into a multiple logistic regression model. Non-significant explanatory variables were removed from the model, and those excluded were re-entered in the model one at a time in a recursive process until all variables in the model were statistically significant and all excluded variables were not statistically significant, using the Wald test or Wald test for trend as appropriate. Pearson’s correlation matrix was used to check for collinearity between all variables and models fitted with and without adjustment for highly correlated variables.

To better understand the strongest effects, we explored associations between reasons given for home delivery and the factors that independently predicted place of delivery using cross-tabulation and chi-squared tests.

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