mHealth intervention to improve the continuum of maternal and perinatal care in rural Guatemala: a pragmatic, randomized controlled feasibility trial

Abstract

Background/objective: Guatemala's indigenous Maya population has one of the highest perinatal and maternal mortality rates in Latin America. In this population most births are delivered at home by traditional birth attendants (TBAs), who have limited support and linkages to public hospitals. The goal of this study was to characterize the detection of maternal and perinatal complications and rates of facility-level referral by TBAs, and to evaluate the impact of a mHealth decision support system on these rates.

Methods: A pragmatic one-year feasibility trial of an mHealth decisions support system was conducted in rural Maya communities in collaboration with TBAs. TBAs were individually randomized in an unblinded fashion to either early-access or later-access to the mHealth system. TBAs in the early-access arm used the mHealth system throughout the study. TBAs in the later-access arm provided usual care until crossing over uni-directionally to the mHealth system at the study midpoint. The primary study outcome was the monthly rate of referral to facility-level care, adjusted for birth volume.

Results: Forty-four TBAs were randomized, 23 to the early-access arm and 21 to the later-access arm. Outcomes were analyzed for 799 pregnancies (early-access 425, later-access 374). Monthly referral rates to facility-level care were significantly higher among the early-access arm (median 33 referrals per 100 births, IQR 22-58) compared to the later-access arm (median 20 per 100, IQR 0-30) (p = 0.03). At the study midpoint, the later-access arm began using the mHealth platform and its referral rates increased (median 34 referrals per 100 births, IQR 5-50) with no significant difference from the early-access arm (p = 0.58). Rates of complications were similar in both arms, except for hypertensive disorders of pregnancy, which were significantly higher among TBAs in the early-access arm (RR 3.3, 95% CI 1.10-9.86).

Conclusions: Referral rates were higher when TBAs had access to the mHealth platform. The introduction of mHealth supportive technologies for TBAs is feasible and can improve detection of complications and timely referral to facility-care within challenging healthcare delivery contexts.

Trial registration: Clinicaltrials.gov NCT02348840 .

Keywords: Barriers to care; Guatemala; Indigenous; Lay midwives; Perinatal monitoring; Referral system; Resource-constrained healthcare; Traditional birth attendants; mHealth.

Fig. 1

A schematic view of the traditional birth attendant obstetric referral strategy. Figure outlines the emergency referral workflow between TBAs and supporting medical team and details the integration of the mHealth system into this workflow

Fig. 2

Example of decision support workflows for facilitating obstetric emergency referral. a Shows an example of workflow on the mHealth platform. In the left-most illustration, the TBA selects the appropriate visit type (prenatal, perinatal, postnatal). The TBA is then guided through a series of pictorial warning signs, with audio prompts as necessary. Checking a warning sign generates an emergency call and text-message to on-call team. b Shows the same pictorial partial workflow through warning signs for a typical perinatal visit for TBAs using the visit placard in the later-access arm (during the non-intervention period), where an emergency telephone number is provided. All images were adapted from existing public sector resources in Guatemala [33]

Fig. 3

Intervention design. TBAs in the early-access arm used the mHealth platform (intervention) through the whole study duration, depicted as a cell phone device in this and related figures. TBAs in the later-access arm provided usual care during the first 7 months of the study (non-intervention period), assisted with a placard-based complication guide (depicted as a notebook sheet in this and related figures), and subsequently crossed into use of the mHealth platform (intervention period)

Fig. 4

Study flow diagram. Enrollment, randomization, and follow-up