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Background: In 2010, the Ministry of Health and Sanitation in Sierra Leone launched their Free Health Care Initiative (FHCI) for pregnant and lactating mothers and children under-5. Despite an increase in the update of services, the inequitable distribution of health services and health facilities remain important factors underlying the poor performance of health systems to deliver effective services. This study identifies current gaps in service delivery across two rural locations served by the same District Health Management Team (DHMT). Methods: We employed a cross-sectional household survey using a two-stage probability sampling method to obtain a sample of the population across two rural locations in Bonthe District: the riverine and the mainland. Overall, a total of 393 households across 121 villages were surveyed in the riverine and 397 households across 130 villages were sampled on the mainland. Maternal health, child health and sanitation indicators in Bonthe District were compared using Pearson Chi-Squared test with Yates’ Continuity Correction across the two areas. Results: Women across the two regions self-reported significantly different uptake of family planning services. Children on the mainland had significantly greater rates of health facility based deliveries; being born in the presence of a skilled birth attendant; completed immunisation schedules; and higher rates of being brought to the health centre within 24 h of developing a fever or a suspected acute respiratory infection. Households on the mainland also reported significantly greater use of treated water and unrestricted access to a latrine. Conclusions: If the government of Sierra Leone is going to deliver on their promise to free health care for pregnant women and their children, and do so in a way that reduces inequalities, greater attention must be paid to the existing service delivery gaps within each District. This is particularly relevant to health policy post-Ebola, as it highlights the need for more contextualised service delivery to ensure equitable access for women and children.
This paper examines where the key gaps exist in the delivery of essential maternal and child health services in Bonthe District. The secondary data analysed for this paper were collected as part of a baseline evaluation for a maternal and child health programme being implemented in Bonthe District as part of the World Vision Sierra Leone, World Vision Ireland, and World Vision UK’s PPA/AIM-Health programme during October-November 2011. As part of AIM-Health/PPA, CHWs are selected, trained and deployed to regularly promote 7 key health intervention messages targeting pregnant women and 11 intervention messages targeting mothers of children under-2. Named the 7–11 strategy, these health intervention messages are delivered through a minimum of ten household visits by a community health worker (CHW). World Vision’s PPA/AIM-Health programme services two locations and one municipality in the riverine (Sittia, Dema, and Bonthe Municipality) and four chiefdoms on the mainland (Imperi, Jong, Sogbeni, and Kpanda Kemoh). A two-stage probability sampling method was applied to obtain a sample of the population across in the riverine and on the mainland. In the first stage of sampling, a list of all the 199 villages from selected chiefdoms of the mainland and 121 villages of the selected riverine chiefdoms was compiled. The probability of a village being selected was therefore set as proportional to the number of households within that village. In this first stage, all 12,037 households on the mainland and 4712 households in the riverine therefore had an equal chance of being selected regardless of whether they contained the target population or not. The total number of households to include was then calculated assuming a confidence level of 95 % (α = 0.05), with an additional 5 % added to account for non-responses. This resulted in a minimum target of 373 households in the riverine, and 391 households on the mainland. In the second stage of sampling, village leaders led field teams to the village centre where a pen was spun to determine the field team’s walking direction. A random number generation table was subsequently used to decide which household was to be visited first. A household was defined in terms of persons who were co-resident and shared common cooking arrangements, and were able to recognise one person as the head of household [18]. In the event that residents were absent from their home or that the target group was not present, field teams were instructed to proceed to the “next” household, which was defined as the one whose front door is closest to the one just visited. Enumerators proceeded to the next household, until the total number of households to be sampled from that village was completed. The survey tool [see Additional file 1] was developed in consultation with the Bonthe DHMT and with assistance from maternal and child health experts within the World Vision Partnership. A total of 30 community health workers (CHWs) were selected as enumerators to participate in the household survey training, hosted by staff from neighbouring health centres and the DHMT. As part of the survey training enumerators were taught how to administer the questionnaire, record responses from participants, verify patient health cards, interpret the mid-upper arm circumference (MUAC) tapes, and weigh and measure children. The survey included questions about household demographics; food intake; child health, including symptoms of acute respiratory infection (ARI), diarrhoea, and fevers, as well as treatment at a health centre within 24 h of the aforementioned symptoms’ onset; child vaccination (calculated for appropriate ages including measles at 9 months, OPV at birth, and 9-months for full immunisation, etc.); maternal care services, including Intermittent Preventive Treatment (IPT), use of insecticide treated nets (ITNs), access to antenatal (ANC), postnatal (PNC) and HIV services; delivery in health centres and in the presence of a skilled birth attendant (SBA); use of any family planning method (calculated by household use rather than individual); hand washing and latrine use. To minimise the risk of response bias, answers were verified through child and maternal health cards (i.e., child vaccinations, ANC visits), where available. The presence of a mosquito net and latrine was verified by the enumerator, as was the cleanliness of the latrine. Piloting of the questionnaire took place in villages not included as part of the final sampling frame. Where appropriate, questions were phrased in binomial (i.e., yes or no) format to facilitate the collection of data. Though the questionnaire was printed in English, training was conducted in a mixture of Krio and Mende and CHWs were instructed to conduct the interview in whichever language they felt best suited the household. To be considered for secondary analysis a household had to contain at least one pregnant woman and/or a child under the age of 5. Interviews were conducted with the child’s primary caregiver, defined as the person who was, “primarily responsible for the health, safety and comfort of that child”. A sample of 393 households across all 121 villages in the riverine, and 397 households across 130 of the 199 villages on the mainland were ultimately represented in the study sample, for a total of 790 households. Quantitative analysis was conducted using SPSS Statistics 17 & 22 (Release Versions 17.0.0 and 22.0.0). Using point prevalence data for various binomial variables (and variables which could be transformed into binomial variables), prevalence data were compared across households in the riverine and the mainland using Pearson Chi-Squared test with Yates’ Continuity Correction. To maximise all data available, missing data was handled using pairwise deletion. Rank variables were compared across the two areas using the Mann-Whitney test. Maternal health, child health and sanitation indicators in Bonthe District were compared across two geographically different areas; the riverine and the mainland. All tests were conducted for 95 % confidence with α = 0.05.
