Our Peers - Empowerment and Navigational Support

People with physical disabilities experience well documented barriers to healthcare access, quality and outcomes. The purpose of this study is to implement and evaluate a 12-month peer health navigator intervention (called Our Peer-Empowerment and Navigational Support) designed to help Medicaid beneficiaries with physical disabilities break down barriers to care through a systematic process of 1) rapport building, 2) identification of healthcare needs and priorities, 3) identification of barriers and supports, 4) co-creation of goal and action plans, and 5) provision of social and informational support. It is hypothesized that people in the interventional trial will experience: improved patient activation, increased social support, increased access to care, increased quality of care, and increased health outcomes compared to people in the control group.

The purpose of this study is to evaluate the preliminary effectiveness and social validity of the OP-ENS (Our Peers - Empowerment and Navigational Support). a 12-month peer health navigator intervention sing a clinical trial design in a sample of community-dwelling adult Medicaid beneficiaries with physical disabilities.

Participants will be recruited through community and clinical networks and screened for eligibility. Participants will be randomly assigned to either the intervention or usual care groups. Participants in the intervention group will be matched with a trained peer health navigator (who is also a person with a physical disability). Using a structure process of barrier identification, goal setting and action planning peer health navigators will work with participants to help address their healthcare needs and concerns, including such things as patient-provider communication, transportation, access to durable medical equipment. It is important to note that peer health navigators are not healthcare providers and do not provide medical or health advice. Participants in the intervention trial are asked to meet with their peer health navigator at least once a month during the study period. To encourage participation in this intervention, progressive micro-incentives are integrated into the study protocol. To promote equipoise, a similar micro-incentive schedule is created for the usual care group.

Data Collection - All participants, regardless of group assignment, will be interviewed at 3 time points (baseline, 6 months, 13 months) using a self-report measures of healthcare access, quality, and outcomes as well as measures of social support and patient activation. Data collectors will be blinded to group assignment and not involved in the delivery of the OP-ENS intervention.

Data Analysis - The investigators will employ mixed effects model using each study outcome as a time-varying dependent variable and treatment group (PHN vs. usual care) as the main fixed effect. Baseline characteristics including race/ethnicity, gender, and socioeconomic status will be entered as time-invariant covariates if baseline group differences are observed (in spite of randomization). Subject intercept will be modeled as a random effect. The null hypothesis will be rejected if a significant group\*time interaction effect is observed. The investigators hypothesize that PHN will have a more favorable trajectory slope of study outcomes than the matched control group. For each hypothesis, post-hoc analysis will be conducted to assess difference in each outcome variable between the two groups at different time points. The mixed models will run using PROC MIXED from of SAS 9.3 (Cary, NC). The method of estimation will be maximum likelihood (ML). A variety of covariance structures (first order regressive, compound symmetry, toeplitz, variance components, unstructured) will be carefully examined and compared for best model fit, Akaike's Information (AIC) and Bayesian Information Criteria (BIC). These statistics are functions of the log likelihood and can be compared across models. As missing data are inevitable in a longitudinal study, values will be imputed where possible using either mean (median) substitute or formal imputation procedures such as EM algorithm if missing data are MCAR (missing completely at random) or MAR (missing at random). If missing data are NMAR (not missing at random), the "pattern mixture" approach will be used to compute a "weighted average" of the parameters that are associated with the missing data to estimate what the data would have been.

Eligibility Criteria:
Inclusion Criteria:

* Medicaid beneficiary
* self-identified as person with a disability
* ability to provide legal consent
* ability to communicate in English

Exclusion Criteria:

* diagnosis of several persistent mental illness, substance abuse, primary diagnosis of pain with narcotic management.