Objective: Health disparity refers to differences in various aspects of health care and health status, among subgroups of the target client population. Of particular interest are information linked health disparity which represents differences in the amount, sharing, and satisfaction with health-related knowledge among subgroups of participants. These differences are of conceptual and methodological importance in the evaluation of cognitive-behavioral interventions aimed at enhancing the clients’ ability to manage their condition, whether acute or chronic. The purpose of this paper is to elucidate the impact of information linked health disparity on the effectiveness of interventions, and to discuss strategies for examining its influence empirically.
Design and Method: This paper is based on a review of conceptual, methodological, and empirical literature related to health disparity.
Problems Posed: Information linked health disparity is reflected in the amount of the health-related knowledge clients have, in the extent to which clients discuss their condition and the treatment plan with their health care providers, and the clients’ satisfaction with health-related knowledge. Differences in these variables are often reported for clients with various socio-demographic backgrounds. These differences could affect the outcomes expected of cognitive-behavioral interventions, directly or indirectly. Information linked health disparity are often highly associated with the anticipated intervention effects such as knowledge, self-care skills, help-seeking behaviors, and health services utilization. Its indirect influence could take place in two ways: 1) the initial differences may interfere with the decision to enter treatment as well as with the dose of the treatment actually received by the clients; and 2) the initial differences are maintained throughout the treatment period, thereby moderating its effects on the anticipated outcomes.
Proposed Solutions: The approach for accounting for the influence of information linked health disparity involves 1) specifying the nature of the relationships among the variables reflecting information linked health disparity, the intervention received, and the expected outcomes; 2) measuring these variables; and 3) testing the relationships using slopes-as-outcomes or multi-level structural equation modeling analysis. The information linked health disparity can be measured at each point of data collection and included in the analysis as a time-variant variable.
Conclusions and Implications: The influence of information linked health disparity presents an extraneous source of variation in the outcomes that threaten the validity of conclusions. Accounting for its influence is of conceptual importance, as it informs who would best benefit from the intervention and how the intervention could be improved to address these differences.
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