Background: Obstetric referrals, otherwise known as maternal referrals constitute an eminent component of emergency care, and key to ensuring safe delivery and reducing maternal and child mortalities. The efficiency of Obstetric referral systems is however marred by the lack of accessible transportation and socio-economic disparities in access to healthcare. This study evaluated the role of socio-economic factors, perception and transport availability in honouring Obstetric referrals from remote areas to referral centres. Methods: This was a cross-sectional study, involving 720 confirmed pregnant women randomly sampled from five (5) sub-districts in the Amansie west district in Ghana, from February to May 2015. Data were collected through structured questionnaire using face-to-face interviewing and analyzed using STATA 11.0 for windows. Logistic regression models were fitted to determine the influence of socio-demographic characteristics and pregnancy history on obstetric referrals. Results: About 21.7 % of the women studied honoured referral by a community health worker to the next level of care. Some of the pregnant women however refused referrals to the next level due to lack of money (58 %) and lack of transport (17 %). A higher household wealth quintile increased the odds of being referred and honouring referral as compared to those in the lowest wealth quintile. Women who perceived their disease conditions as emergencies and severe were also more likely to honour obstetric referrals (OR = 2.3; 95 % CI = 1.3, 3.9). Conclusion: Clients’ perceptions about severity of health condition and low income remain barriers to seeking healthcare and disincentives to honour obstetric referrals in a setting with inequitable access to healthcare. Implementing social interventions could improve the situation and help attain maternal health targets in deprived areas.
A cross-sectional study, which employed quantitative methods, was conducted from February to May 2015 in the Amansie West district in the Ashanti region, Ghana. The district, which has Manso Nkwanta as its capital is one of the 35 districts in the region. It is about 1,364 sq km in size and uniformly rural and deprived. The topography is characterised by waterlogged and dust during the wet and dry seasons respectively. The large expanse of land coupled with rugged relief makes transporting emergencies cases very difficult and sometimes impossible. It takes an average of 5–7 h to transport emergency case from the remote areas to the district hospital at Agroyesum or nearby district hospitals. There is a burden of heavy workload on the few available health staff. The district has a projected population of 149,437 as at 2014 with an annual growth rate of 2.7 %. There are currently 22 health facilities and 54 functional CHPS zones. The doctor to population ratio is 1: 74,719; nurse to population ratio 1:2, 767 and midwife to women in fertility age (WIFA) ratio is 1:4,528. There is a high rate of illiteracy (70 %) and a high dropout rate in schools especially among girls. These conditions are possible risk factors for teenage pregnancies and obstetric complications [16]. The study population consisted of diagnosed second trimester pregnant women presenting at the antenatal clinic who provided written informed consent and satisfying the group-based inclusion criteria. A multi-stage sampling technique was employed for this study. The antenatal clinics in the study communities were selected from numbers 1 to 10, without replacement by research assistants. At the selected community antenatal clinic, systematic random sampling, guided by a sampling interval (estimated as the required sample size divided by the total attendants), was used to select the required subjects for the study. The sample size was estimated with recourse to Cochran (1977) [17]. Using this formula, and assuming access to referral service at 50 % and a non-response of 20 %, a total of 720 pregnant women were sampled from five towns; one each from the five sub-districts to participate in the study. The required respondents from selected health facilities were proportional to size of total eligible population per community. The distribution of respondents according to the sub district was Manso Nkwanta 120, Edubia 141, Agroyesum114, Antoakrom 140 and Esuowin 205, Table 1. Sample distribution from the study communities A structured questionnaire was used to elicit information on respondents’ socio-demographic and socio-economic characteristics, pregnancy history, experiences and perceptions of obstetric referrals. The questionnaires were pretested among 20 respondents in a sub-district with similar characteristics like the study sub-districts to check for clarity, consistency, and acceptability. The pretested sub-district was excluded from the study to avoid spill over effects. Questionnaires were interviewer administered and the interview results from the field were checked for completeness and internal errors prior to data entry. Data entry and preliminary analysis were done in EPIDATA and more detailed analysis done with Stata 11.0 for windows [18]. Descriptive statistics were summarized and displayed in graphs and charts. Verified data were cleaned on a regular basis through running programmes on legal values and consistency checks. Data analysis consisted of descriptive statistics, checking of potential assumption violations and testing of the study hypothesis. Continuous variables were compared using student t-test and discrete variables analyzed using χ2 in r X n tables. Logistic regression models were fitted to determine the influence of socio-demographic characteristics and pregnancy history on obstetric referrals, and to control for covariates. The binary outcome (obstetric referral) was based on respondents’ response to the question on whether they honoured referrals for obstetric reasons to the secondary level or not. Two models were fitted; model 1 consisted of only socio-economic variables (education, employment, length of stay in community, household wealth and self-rated socio-economic status) and model 2 involved the addition of pregnancy history (miscarriages and still births) and perception of disease condition. Variables in the multivariable analysis were selected based on p-value of less than 0.250 in the univariable analyses. For all analysis, a 2-tailed α with p < 0.05 was considered statistically significant.
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