Methods
The trial reported here compares maternal outcomes between two wards randomly allocated to receive home visits immediately and two wards allocated to receive home visits after a delay of 1 year. We have described the overall trial methods in detail elsewhere.14
The study took place in Toro Local Government Authority in Nigeria’s north-eastern Bauchi State. The state has around 5 million residents, the main religion is Islam, family sizes are large and polygamy is common. Some 73% of women in Bauchi have no education, compared with 38% nationally.4 Toro is the largest Local Government Authority, with a 2014 population of 437 000. More than 95% of the population is Muslim and predominantly Hausa (80%) or Fulani (12%) ethnicity. A 2013 survey found 22% of women in Bauchi and 27% in Toro had a skilled attendant for their last delivery, and 57% in Bauchi and 71% in Toro had to pay in cash or kind when attending ANC in a government facility.16 Also, 82% of women in Bauchi and 60% in Toro did not reduce heavy work before the last trimester of pregnancy, and 17% in Bauchi and 16% in Toro experienced domestic violence during their last pregnancy.16
Participants and intervention
All women of childbearing age (14–49 years) in all households in the intervention wards were eligible for the study. Each ward included urban, rural and rural remote communities. Among these women, all those who became pregnant during the study period were visited at home several times during their pregnancies; their husbands were also visited during the pregnancies.
The protocol provides a full description of the intervention.14 Each home visit team of one woman and one man covered around 300 households and visited every household every 2 months. The female home visitors typically visited the households and followed the pregnant women during daylight hours, while the male home visitors typically visited the same households and spoke to the spouses of the pregnant women in the evenings or at weekends, when the men were at home. We recruited the home visitors mostly from the intervention communities, and trained and evaluated them before they began the work. On the first visit, the female visitor asked about household demographics and socioeconomic status. On each subsequent visit, she checked how many women of childbearing age were in the household, noted how many were pregnant, and followed those registered as pregnant with a surveillance questionnaire and discussion about the four issues related to pregnancy risk in a previous study in the state: heavy work in pregnancy, experience of domestic violence, lack of communication with the spouse and lack of knowledge about pregnancy danger signs.13 Male visitors separately interviewed and held discussions with the partners of the pregnant women, also every 2 months. The intention was to provoke household discussion and action on the risk factors. A visit after delivery recorded information about the whole pregnancy and delivery.
The home visitors entered interview responses directly into GPS-enabled android handsets preloaded with information for the home visitors to share with pregnant women and their spouses, along with instructions for referring pregnant women who reported danger signs to a local clinic. They uploaded records to a central server after each visit. We used open-source Open Data Kit software for the electronic data collection.17 The GPS locations included in the uploaded records allowed us to check that the home visitors actually conducted interviews in the intended households.
The home visitors did not routinely encourage pregnant women to attend for routine ANC or to deliver in health facilities. However, recognising that some visited women might report danger signs during their pregnancies, home visitors’ training included this eventuality and their handsets carried a decision aid of what to do in different cases, including when to refer to a health facility. The training stressed the importance of conducting all interviews with privacy and covered practical ways of ensuring privacy in the household setting in Bauchi. It also covered how to handle any distress caused by discussion of sensitive topics, such as domestic violence.
The study conformed to the principles embodied in the Declaration of Helsinki. The research team discussed the home visits with the leadership of all communities in the participating wards to get their approval to proceed. We treated all responses from participants as confidential, with no names or identifying information recorded.
Outcomes
The primary outcome was maternal morbidity during pregnancy and within 6 weeks after delivery, as reported by women after completed pregnancies. The questionnaire asked about pregnancy complications including severe headaches, swelling of hands and feet, dizziness or blurred vision, high blood pressure (if measured during the pregnancy), convulsions and vaginal bleeding during pregnancy. It also enquired about perineal trauma (cut or tear) during delivery and delivery by Caesarean section. Postpartum complications included wound opening, high fever and smelly discharge. We defined postpartum sepsis as the presence of any one of these three complications.
Behavioural and knowledge indicators specifically targeted by the visits were heavy work during pregnancy, experience of verbal and physical domestic violence, communication with the spouse about pregnancy and delivery, and knowledge of danger signs during pregnancy and delivery. Indicators of access to healthcare were at least one ANC visit to a facility, at least one blood pressure measurement, urine tested at least once, delivery attended by a trained health worker (community health worker, nurse, midwife or doctor), delivery in a health facility, and a postnatal visit within 6 weeks.
In the intervention wards, the home visitors followed pregnant women with bimonthly visits and administered an electronic questionnaire after delivery; we included in the present analysis all post-delivery questionnaires completed up to 31 December 2017. In the pre-intervention wards, the home visitors administered the same questionnaire to women in the baseline visit, asking about completed pregnancies in the last 12 months.
Sample size
Our sample size calculations used the clinical trials simulator of Taylor and Bosch.18 Our 2013 study in the same local government authority16 found 60% of women did not reduce heavy work in pregnancy and 58% reported postpartum infection or another serious complication of pregnancy. At this frequency, with an estimated 2880 births in each ward over a 2-year period, our study could detect a 20% reduction in complications (80% power at the 5% level, k=0.05) between two intervention wards and the two pre-intervention wards.
The study was not powered to show an impact on maternal mortality, although this should be measurable with later roll-out to other local government authorities. The Toro MMR (around 800 per 100 000 live births) implies around 35 maternal deaths in each ward over 2 years. The wards with home visits would have to reduce mortality by 35% to be detected with 80% power at the 5% level (k=0.06).
Randomisation and masking
At the beginning of the study, random allocation of six participating wards in Toro Local Government Authority generated three groups of two wards each. An epidemiologist not involved in the fieldwork (NA) generated the allocation sequence. We first divided the six wards into two sets, geographically apart. For each group of two wards, we randomly selected one ward from each of these two sets. We report here the comparison of outcomes at 1 year between the first group of two wards (intervention wards) and the second group of two wards (pre-intervention wards). The third group of two wards will receive visits after a further year’s delay.
There was no possibility to conceal allocation once the intervention began. The home visitors conducting interviews for measuring outcomes could not be blinded to group assignment but, hired simply to visit households and interview participants, they did not have any reason to interview differently in intervention and control sites.
Statistical methods
Ward was the unit of randomisation, intervention and analysis. We used the Mantel-Haenszel procedure19 adjusted for clustering (Lamothe method)20 to examine differences between the intervention and pre-intervention wards at baseline. This first assessment of the impact of home visits contrasted primary outcomes after 1 year between the intervention and pre-intervention wards.
The principal analysis of the primary outcomes used a t-test in an intention-to-treat analysis of cluster-specific rates.21 With cluster as unit of analysis, we estimated relative risk reduction (RRR) as one minus the relative risk (RR), using variance of the RR (Delta method) to estimate CIs. We estimated the number needed to treat (NNT) as the reciprocal of risk difference (RD), and intra-cluster correlation (ICC) by dividing the between-cluster variance by the between-cluster and within-cluster variance across the control series.22
Prespecified ancillary analyses used generalised estimating equations for logistic regression (exchangeable matrix, 1000 iterations), assuming an exchangeable correlation structure within wards, to incorporate the cluster design, any differences at baseline, and any differences in use of health services during the pregnancy and delivery.23 This analysis examined the possibility that these differences between intervention and pre-intervention wards explained the findings. We wanted to be sure any impact of home visits was not simply due to baseline differences, or to increased use of health services during pregnancy and delivery.
A supplementary analysis examined associations between reports of pregnancy complications and attendance at ANC and delivery in health facilities.