Comparing Program Options for Latinos With Diabetes

BACKGROUND AND SIGNIFICANCE:

Diabetes is a national health priority but diabetes risk is extremely high for Latinos from low-income households. Health guidelines recommend that individuals learn strategies to self-management their diabetes, but getting people to adopt required lifestyle changes is challenging and many people are not able to prevent their pre-diabetes from escalating or to control their diabetes. Systematic reviews show that culturally competent self-management programming can significantly improve diabetes outcomes, and different models for culturally competent programming have been developed.

STUDY AIMS:

The goal of this study is to compare the effectiveness of 2 distinct evidence-based models for culturally competent diabetes health promotion. The hypothesis is that the program model that interfaces most synergistically with patient's culture and everyday life circumstances will have the best diabetes health outcomes.

Aim 1: Characterize the ways that three culturally competent diabetes self-management programs interface with patient culture and socioeconomic context

Aim 2: Measure and compare improvement in patient capacity for diabetes self-management.

Aim 3: Measure and compare patient success at self-management Study Description

OVERALL STUDY DESIGN:

This study follows NIH standards for mixed-method research by integrating data from quantitative and qualitative components of the study in an iterative fashion. The sample size and power estimates are based on realistic evaluation of effect size. Data collection will involve programmatic assessments, interviews, focus groups, surveys, and testing for A1c, BMI, and stress. The research team has the expertise and experience in patient-engaged research necessary to conduct the proposed study and the investigators have an institutional infrastructure that supports the academic and community partnerships necessary for this study.

MAIN COMPONENTS OF THE INTERVENTION AND COMPARATORS:

This study compares 2 diabetes self-management programs that serve a large Latino patient population from low-income households in Albuquerque, New Mexico

• The Diabetes Self-Management Support Empowerment Model
• The Chronic Care Model

PRIMARY & SECONDARY OUTCOMES:

PRIMARY: Improved capacity for diabetes self-management measured through improvements in diabetes knowledge and diabetes-related patient activation.

SECONDARY: Successful diabetes self-management.

ANALYTIC METHODS:

Descriptive statistics will be calculated to summarize patient characteristics. Means and standard deviations or medians and quartiles will be calculated for continuous variables and will be compared across site by ANOVA or Kruskal-Wallis test, depending on the distribution of the data. Frequencies and percentages will be calculated for categorical variables and will be compared with the chi-square test or Fisher's exact test, as appropriate. Significant differences will be noted and to adjust for possible confounding, those variables will be considered for inclusion as covariates in the analyses for the primary and secondary outcomes in addition to other clinically meaningful variables and their interactions. It is expected that patient characteristics to be similar across the two treatment sites; however, to control for potential differences in the populations, the investigators will adjust for potential confounding covariates by using propensity scores to stratify subjects into groups based on the probability that they attended a particular treatment site given particular demographic characteristics including sex, age, primary language, level of education, nativity, and type of insurance. Patients will be grouped by quintile of propensity score for a total of five strata and each will be analyzed for the primary and secondary outcomes independently. Analyses will be performed in standard statistical software. Propensity score matching allows for causal inference in our non-experimental settings by selecting similar subsets of comparison units between treatment groups across a high-dimensional set of pretreatment characteristics. For qualitative data, the investigators will conduct a rigorous, disciplined, empirical analysis based on plausibility, credibility and relevance. The investigators will conduct a theory-driven qualitative content analysis, reading through transcripts to identify conceptual categories and patterns related to specified domains of inquiry, creating a qualitative codebook, and developing conceptual summaries for each transcript. Following review and summary, the investigators will code transcripts for systematic themes and subthemes and explore interconnections between theme categories and develop a holistic interpretation of the data ("constant comparison").

Inclusion criteria

Patient Participants:

• Adults (men and women) who have been identified by a provider as having pre-diabetes (A1c 5.7-6.4) or diabetes (A1c 6.5 or above)
• Enter one of the two diabetes programs during the study
• Self-identify as "Latino"
• Can identify a social support or key member of their social network who will agree to participate with them
• Are not pregnant (participants who become pregnant during the study will be excluded)
• Have household income 250% of the Federal Poverty Level (FPL) or below.

Social support participants:

• Adults
• Individuals who are identified by the patient participants as their social support

Exclusion criteria

• Adults unable to consent
• Individuals who are not yet adults (infants, children, teenagers)
• Prisoners
• Pregnant women

Participants who become pregnant during the study will be excluded. At enrollment and before each data collection appointment, female patient participants will be screened to specifically exclude those who are pregnant. There is no risk to a pregnant woman or a fetus entailed in participation in this study because the study only involves data collection from individuals who have been instructed to participate in a diabetes self-management program by their provider. However, pregnant individuals will be excluded because pregnancy could impact outcomes in a way that will influence scientific analysis of diabetes self-management.