Menu
Menu
Menu
Menu
Maternal and child mortality rates remain unacceptably high globally, particularly in sub-Saharan Africa. A popular approach to counter these high rates is interventions delivered using mobile phones (mHealth). However, few mHealth interventions have been implemented nationwide and there has been little evaluation of their effectiveness, particularly at scale. Therefore, we evaluated the Rwanda RapidSMS programme – one of the few mHealth programmes in Africa that is currently operating nationwide. Using interrupted time series analysis and monthly data routinely reported by public health centres (n = 461) between 2012 and 2016, we studied the impact of RapidSMS on four indicators: completion of four antenatal care visits, deliveries in a health facility, postnatal care visits and malnutrition screening. We stratified all analyses based on whether the district received concurrent additional supports, including staff and equipment (10 out of 30 Districts). We found that community health workers in Rwanda sent more than 9.3 million messages using RapidSMS, suggesting the programme was successfully implemented. We found that the implementation of the RapidSMS system combined with additional support including training, supervision and equipment provision increased the use of maternal and child health services. In contrast, implementing the RapidSMS system alone was ineffective. This suggests that mHealth programmes alone may be insufficient to improve the use of health services. Instead, they should be considered as a part of more comprehensive interventions that provide the necessary equipment and health system capacity to support them.
The development and piloting of the RapidSMS system has been discussed elsewhere (Ngabo et al., 2012). The RapidSMS system is a two-way communication system between community health workers (CHWs) and the Ministry of Health. CHWs are the first point of contact with the health system and create a link between communities and health facilities. Each village in Rwanda had three elected CHWs, one of whom oversees maternal and child health, and all of whom could access the system. In brief, CHWs in Rwanda were given mobile phones to report data on maternal and child health indicators using text messages. RapidSMS data were collected during pregnancy, and from birth until 2 years of age, and included a number of indicators: ANC, delivery, maternal mortality, postnatal care, anthropometric measurements and child mortality. The system generated automatic reminders for clinical appointments that were sent to the CHW, including ANC, the probable delivery date and postnatal care, with the aim of increasing routine care attendance and the proportion of health facility deliveries. Mothers were not messaged directly. RapidSMS was also designed to quickly link mothers to emergency obstetric care by notifying ambulance services (Ngabo et al., 2012). Simultaneously with this national scale up, UNICEF, using a Korea International Cooperation (KOICA) Grant, provided more comprehensive support to complement RapidSMS in 10 of 30 districts. These districts were selected based on poor maternal and child health indicators that were available at the district level (such as the facility delivery rate and infant mortality) and their distance to the capital, Kigali. This programme included the recruitment of two NGOs to support districts with the implementation and use of RapidSMS, ongoing training sessions for CHWs on providing home-based care, and providing supervision meetings with health centres every calendar quarter. It also included the provision of equipment to District Hospitals and Health Centres for treating newborns, including CPAP machines, radiant warmers, bed nets, suction devices and other supplies. We used data from two sources: the database of messages sent by CHWs, and the Rwanda Health Management Information System (HMIS), which contains routinely collected facility-level data on the provision of maternal and child health services. We used data on all messages sent by CHWs through June 2016 from the RapidSMS database. Data in the HMIS system are collected from each health facility in Rwanda by a designated individual, leading to a very high rate of data completeness (Nisingizwe et al., 2014). As the monthly reporting forms for the HMIS system were substantially changed in January 2012, we used data from that date through June 2016. We studied the impact of RapidSMS on the following process of care indicators using outcomes data from the HMIS database: Each of these indicators was reported monthly by each health facility. To allow comparisons between districts and account for population growth over time, we calculated per-capita monthly rates using estimated catchment populations for each health centre derived from the 2012 Census data. We used interrupted time series analysis, one of the strongest quasi-experimental research designs (O’Keeffe et al., 2014; Moscoe et al., 2015). This method has the distinct advantage of accounting for pre-existing trends in the outcomes, which are very common in the above indicators (Wagner et al., 2002). As RapidSMS was scaled-up at different times across Rwanda, we determined the month in which CHWs from each health facility first sent 50 or more messages. For each facility, we then obtained data from HMIS for 14 months prior. We chose this time length to include as many facilities as possible, as scale-up started in March 2013. Of the 481 health centres in the HMIS system, 461 health centres (96%) had data available for at least 24 months after they initiated use of the system. We excluded the 20 health centres that did not cross the 50 messages threshold before July 2014 as we did not have adequate post-intervention data. All analyses were thus conducted using ‘study time’, which is the number of months relative to each health centre’s index month. Our interrupted time series models took the following form: Where ‘time’ represented the month in study time (i.e. 1, 2, 3…), ‘RapidSMS’ indicated whether the health facility had implemented RapidSMS, and ‘post’ represents the number of months since implementation in month t. Any immediate change in the outcome would be captured by β2 and any change in the ‘trend’ of the outcome after RapidSMS initiation would be captured by β3. We stratified our analysis based on the 10 UNICEF-supported districts and the other 20 districts within Rwanda. For each outcome, we included only health facilities that reported complete data at every time point. This included nearly all health centres for ANC, deliveries and malnutrition screenings (430, 426 and 426, respectively) and fewer for postnatal care visits (88). As our observations may have been correlated over time, we used a generalized least squares model with a first-order autoregressive structure (Wagner et al., 2002). In order to aid interpretation of rate changes, we used established methods to calculate percentage changes at one year following RapidSMS implementation (Zhang et al., 2009).
N/A
