A randomized controlled trial is currently being conducted in Geisinger prenatal clinics in Pennsylvania to evaluate the impact of integrating obstetric care with the Women, Infants, and Children (WIC) program on enrollment rates among low-income pregnant individuals. The study, registered with ClinicalTrials.gov (NCT06311799), aims to address high rates of preterm birth, low birth weight, and infant mortality in the region by enhancing food security through improved WIC enrollment.
Study Design and Intervention Models
The trial randomizes approximately 240 low-income pregnant participants into one of four intervention arms:
- Clinic Model: Standard prenatal care with basic WIC information.
- Clinic-WIC Model: Standard care plus a digital referral to WIC via the electronic health record (EHR).
- Clinic-RDN Model: Standard care plus a digital referral to a registered dietician (RDN) for heart-healthy eating and food resource management counseling.
- Clinic-WIC-RDN Model: Combines digital referrals to both WIC and RDN counseling.
The study design allows for evaluation of the independent and combined impacts of direct WIC connection and dietary counseling on WIC enrollment rates, food security, and food intake behavior. Participants are blinded to their assigned condition, and outcome assessors are also blinded.
Recruitment and Participant Criteria
Participants must be at least 18 years old, English-speaking, confirmed pregnant, intending to deliver at a Geisinger facility, and have public or no insurance, serving as a proxy for lower income status (up to 185% federal poverty level). Exclusion criteria include ineligibility for WIC, pre-existing WIC enrollment, private insurance, or unwillingness to participate for up to 12 months. Recruitment prioritizes early pregnancy to maximize the potential benefit of food provision and nutrition interventions.
Intervention Components
WIC Referral
In the Clinic-WIC models, a digital referral to WIC is facilitated through Neighborly (FindHelp(C) 2023), a HIPAA-compliant social health access referral platform integrated into the EHR. Clinical staff can access Neighborly to identify social care programs and, with patient consent, make direct referrals. Alerts within the EHR prompt clinical staff to submit digital referrals to WIC, streamlining the referral process. Initial low adoption of the alerts led to their transition into “hard stops,” requiring nurses to address the alert before concluding the clinical encounter. A second region of obstetric clinics was added to the study to enhance intervention exposure.
RDN Counseling
The RDN Model connects patients with a study RDN for heart-healthy eating and food resource management telehealth counseling. Referrals to RDNs are prompted via EHR alerts, generating in-basket messages to the RDNs, who then contact participants to schedule sessions. Counseling, conducted via telemedicine, focuses on food resource management skills, heart-healthy eating, and provision of cooking utensils. Study RDNs have prior experience in delivering telemedicine interventions focused on food resource management and nutrition behavior.
Data Collection and Measures
Data collection includes WIC certification data (enrollment, retention, adherence), EHR data (health indicators, appointment adherence), and patient questionnaires (food security, self-efficacy in food resource management, diet quality, binge eating frequency, eating competence, and general health status). Semi-structured qualitative interviews will also be conducted with a subset of participants to gather insights on their experiences. Provider data includes surveys assessing acceptability, appropriateness, and feasibility of the intervention.
Study Outcomes
The primary outcome is the difference in WIC enrollment rates between the WIC information-only arms and the digital referral arms at 6 months. Secondary outcomes include WIC retention and adherence, food security, self-efficacy in food resource management, diet quality, and indicators of health status. The study will also evaluate differences between non-RDN and RDN models and assess factors contributing to intervention implementation.