The purpose of the study is to explore ways to improve maternal and child outcomes in the ethnic Maya speaking the Kaqchikel language population, which experiences wide disparities in health care access and outcomes when compared to other inhabitants of Guatemala who are not ethnically Maya and live in other parts of the country.

While advances in medical care have reduced mortality rates across the globe, the same cannot be said for perinatal mortality. This is particularly true of Low and Middle Income Countries (LMICs), which contribute by far the largest proportion of the estimated 5.4 million perinatal deaths annually. Key reasons for this include lack of systematic screening, lack of early health advice-seeking, lack of training for the healthcare workers involved in the healthcare delivery chain and a lack of robust referral. To address these issues, the investigators will introduce and evaluate a scalable mobile health (mHealth) referral system in Guatemala, one of the poorest countries in Latin America with one of the highest perinatal mortality rates. The system combines several key innovations which have been developed over the last 10 years. First, the investigators will introduce a low cost (under $30) ultrasound screening tool, with a blood pressure cuff and pulse oximeter, from which data is fed directly to a smartphone to produce an on-the-spot assessment of the health of the fetus and mother, using a step-by-step pictorial guide (also on the phone). Second, the investigators will implement a mobile phone-mediated medical record and referral system that allows users of both smartphones and basic mobile phones to upload data into a standardized medical record system already in operation in Guatemala. The data will allow healthcare workers to identify or enroll patients, review their histories, and schedule future screenings or follow-up visits, as well as equip the healthcare providers with the necessary information to assess needs, resource allocation and efficacy of treatment or personnel. Thirdly, the investigators will introduce a training protocol for lay midwives to provide routine screening with the system for risk factors such as high blood pressure, fetal growth restriction and fetal distress, and to promote postnatal care coordination. Importantly, this shared system will facilitate communication between the obstetrical care team and the postnatal child care team, which currently represents one of the most pressing care coordination challenges in rural Guatemala. By bringing together engineers, physicians, public health workers, anthropologists, and local nongovernmental organizations to co-design the system around the needs of the existing healthcare infrastructure and the local population, the investigators will create an intuitive system which relieves the burden of data collection, improves diagnostic capabilities, and assists with rapid and accurate referral. The system will also facilitate inter-facility research by introducing a standard medical record protocol.

Inclusion Criteria for Midwives:

• Wuqu' Kawoq's catchment area
• Bilingual speakers of Spanish and Kaqchikel Maya
• Willing to give consent and be trained on the mHealth technology
• Must be under the age of 65
• Must be 18 years-old or older

Exclusion Criteria for Midwives:

• Outside Wuqu' Kawoq's catchment area
• Not a bilingual speaker of Spanish and Kaqchikel Maya
• Over the age of 65
• Under the age of 18
• Unable to give consent and be trained on the mHealth technology

Inclusion Criteria for Pregnant Women:

• Must be 18 years-old or older
• Must be pregnant
• Must have a midwife that has been recruited for the study
• Bilingual speakers of Spanish and Kaqchikel Maya
• Willing to give consent

Exclusion criteria for pregnant women

• Under 18 years of age
• Not pregnant
• Does not have a midwife in the study
• Is not bilingual in Spanish and Kaqchikel Maya
• Unable to consent

Data will be de-identified and shared through PhysioNet.org. Raw 1-D ultrasound data plus associated demographics will be available.

Data is estimated to become available in 2022 after compilation and analysis is complete.

Data will be publicly available via PhysioNet.org with no restrictions on analyses.

The baseline analysis population includes all TBAs and pregnant women for whom demographic data was collected.

Protocol-related adverse events for pregnant participants were collected during the 12 month intervention period.

Only protocol-related serious and non-serious adverse events for pregnant participants were collected. Adverse events were not collected from the traditional birth attendants as adverse events related to the study intervention were not expected to impact these participants.