Multimorbidity from Diabetes, Heart Failure, and Related Conditions: Assessing a Panel of Depressive Symptoms as Both Formative and Reflective Indicators of a Latent Trait

Abstract

Through exploring specific conditions (diabetes, heart failure, related vascular/metabolic diagnoses) and their multimorbidities, I develop a more thorough means to adjust confounders of clinical targets within main or interactive contexts in epidemiological panel studies. Regression-based multiple indicators-multiple causes (MIMIC) models combine multiple or moderated regression and confirmatory factor analysis. In a novel specification, each of twenty depressive symptoms is both a “formative” (causal) indicator and a “reflective” (effect) indicator of a latent trait (Depression). Although both indicators provide identical information (under different variable names), formative indicators provide “exogenous” information (outside the model) to estimate, within groups or subgroups, “endogenous” effects (recovered by the model) from the latent trait and its reflective indicators. Formative indicators within the multiple regressions constitute comprehensive proxies for unspecified confounders by completely mediating all unspecified confounder effects on the endogenous latent trait and its reflective indicators, the latter estimated through confirmatory factor analysis. Findings of symptom clusters of Depression in these specific conditions, and in subgroups that capture their synergies, corroborate parallel MIMIC models with instrumental variables that specify several known confounders, but suggest some confounding biases remain. All multimorbidities involve synergy from co-occurring diabetes and heart failure. There may be opportunities to target screening and optimize metformin treatment for these co-occurring conditions. This strategy avoids the need to specify all confounders, which may not be possible or verifiable.

Keywords

• diabetes
• heart failure
• depression
• symptom clusters
• metabolite panels
• formative indicators