Partner Involvement in Type 2 Diabetes Self-Management: A Mixed-Methods Investigation

Researchers have recognized the role of social environment in diabetes management, with substantial attention directed toward spouses or romantic partners of people with diabetes. However, the specific ways in which partners are involved have not been articulated. This study, which included 207 couples in which one person was recently diagnosed with type 2 diabetes, used a mixed-methods approach to assess types of partner involvement in diabetes management. First, different types of partner involvement were qualitatively identified from audiotaped interviews, and links between qualitative findings and demographics were examined. Next, qualitative codes were compared to quantitative measures of partner involvement. Finally, relations of qualitative codes to relationship quality and diabetes outcomes were assessed. Qualitative analyses identified three ways in which partners were involved in diabetes management (support provision, collaboration, and controlling behavior) and two ways in which they were not involved (independent coping and disengagement on the part of the person with diabetes). Participants with diabetes perceived less partner involvement than their partners. Comparisons with quantitative measures revealed that collaboration was distinct from partner support. Reports from participants with diabetes of collaboration, but not partner support, were connected to higher relationship quality and lower A1C, whereas partner reports of collaboration were related to better self-care. Diabetes disengagement was associated with poorer relationship and behavioral outcomes. These findings underscore the varied ways in which partners are and are not involved in diabetes management and suggest that collaboration is more beneficial than social support in terms of relationship quality and diabetes outcomes.

Partner Involvement in Type 2 Diabetes Self-Management: A Mixed-Methods Investigation

Researchers have recognized the role of social environment in diabetes management, with substantial attention directed toward spouses or romantic partners of people with diabetes. However, the specific ways in which partners are involved have not been articulated. This study, which included 207 couples in which one person was recently diagnosed with type 2 diabetes, used a mixed-methods approach to assess types of partner involvement in diabetes management. First, different types of partner involvement were qualitatively identified from audiotaped interviews, and links between qualitative findings and demographics were examined. Next, qualitative codes were compared to quantitative measures of partner involvement. Finally, relations of qualitative codes to relationship quality and diabetes outcomes were assessed. Qualitative analyses identified three ways in which partners were involved in diabetes management (support provision, collaboration, and controlling behavior) and two ways in which they were not involved (independent coping and disengagement on the part of the person with diabetes). Participants with diabetes perceived less partner involvement than their partners. Comparisons with quantitative measures revealed that collaboration was distinct from partner support. Reports from participants with diabetes of collaboration, but not partner support, were connected to higher relationship quality and lower A1C, whereas partner reports of collaboration were related to better self-care. Diabetes disengagement was associated with poorer relationship and behavioral outcomes. These findings underscore the varied ways in which partners are and are not involved in diabetes management and suggest that collaboration is more beneficial than social support in terms of relationship quality and diabetes outcomes.

Bayesian variable selection in hierarchical difference-in-differences models

A popular method for estimating a causal treatment effect with observational data is the difference-in-differences model. In this work, we consider an extension of the classical difference-in-differences setting to the hierarchical context in which data cannot be matched at the most granular level. Our motivating example is an application to assess the impact of primary care redesign policy on diabetes outcomes in Minnesota, in which the policy is administered at the clinic level and individual outcomes are not matched from pre- to post-intervention. We propose a Bayesian hierarchical difference-in-differences model, which estimates the policy effect by regressing the treatment on a latent variable representing the mean change in group-level outcome. We present theoretical and empirical results showing a hierarchical difference-in-differences model that fails to adjust for a particular class of confounding variables, biases the policy effect estimate. Using a structured Bayesian spike-and-slab model that leverages the temporal structure of the difference-in-differences context, we propose and implement variable selection approaches that target sets of confounding variables leading to unbiased and efficient estimation of the policy effect. We evaluate the methods’ properties through simulation, and we use them to assess the impact of primary care redesign of clinics in Minnesota on the management of diabetes outcomes from 2008 to 2017.