Impact of comorbidity on health-related quality of life among type 2 diabetic patients in primary care

Abstract

Background: Health-related quality of life (HRQL) is an important outcome for chronic diseases such as diabetes mellitus that is associated with complications, comorbidities, and lifelong care. Objectives: The present study aims to explore the impact of comorbidities on the different dimensions of HRQL among type 2 diabetic patients attending primary care. Methods: A total of 912 type 2 diabetic patients attending primary care centers in India were assessed using a predesigned and pretested questionnaire – Diabetes Comorbidity Evaluation Tool in Primary Care. The HRQL was measured by physical and mental health summary scores [physical component summary (PCS) and mental component summary (MCS)] of the Short Form Health Survey 12. The associations of sociodemographic variables and clinical variables with PCS and MCS were assessed, and a minimal difference of 5 in the scores (on a scale of 0–100) was kept as clinically relevant difference for this study. Mean differences in mental (MCS) and physical (PCS) scores of quality of life by number and type of comorbid conditions in type 2 diabetic patients were calculated. Result: The presence of comorbid conditions was associated with lower scores of PCS and MCS (P < 0.001). Significant reduction in HRQL was found with increase in number of comorbid conditions, and negative association was established between the number of comorbidities and the PCS (r = −0.25, P < 0.0001) and MCS scores (r = −0.21, P < 0.0001). Among comorbidities, acid peptic disease, chronic lung disease, visual impairment, depression, and stroke had significantly and clinically relevant reduced scores. Duration of diabetes, use of insulin, and obesity were also associated with poor HRQL. Conclusion: Comorbidities considerably impair the HRQL among type 2 diabetic patients. National programs designed for diabetes management should also take into account the challenges of coexisting chronic conditions and its substantial effect on HRQL.

Comorbid Chronic Diseases and Health-related Quality of Life in the Obese Population: a Longitudinal Analysis of a Nationally Representative Household Survey in Australia

“…Further, this current study revealed that a higher number of chronic diseases was associated with a reduction in scores in all eight dimensions of the SF-36. Similar ndings have been highlighted elsewhere that studied the association between comorbid diseases and HRQoL [37][38][39].…”

Non-communicable diseases care during COVID-19 pandemic: A mixed-method study in Khurda district of Odisha, India

“…Previous studies in Odisha found that one-third of patients attending primary care settings had multiple chronic conditions (two or more) 28 29 30 31 . Similar findings have been observed in a chart review of the morbidity profile of urban primary care patients 29 .…”

Emerging Multimorbidity Patterns and Its Linkages With Selected Health Outcomes Among Working-Agegroup Population: Findings From Odisha, India

“…The ndings highlighted that age, ethnicity, and place of residence were found to be signi cantly associated with the being in one of the multimorbid groups. The ndings suggest that with increasing age [ (35)(36)(37)(38)(39)(40)(41)(42)(43)(44)(45)(46)(47)(48)(49) increased the risk of being in the "Hypertension-Diabetes-Arthritis' group compared to 'low-co morbidity' group. Whereas, residing in urban areas [AOR=0.72 (0.53, 0.98)] reduced the risk of being in the "Hypertension-Diabetes-Arthritis' group by compared to 'low co morbidity' group.…”

“…Multimorbidity thus requires a multifaceted approach which can be well integrated and approached with ease. [39,40,41,42] Strengths and limitations: Most of studies on multimorbidity in India are from the older population, anticipating the growing prevalence of multimorbidity among working age group studying the prevalence, patterns, and its health impact in working age population are pivotal. Also, to the best of our knowledge this is rst study from India, to identify the pattern of diseases using latent class analysis among working class group as most of the studies usually report most common dyad and triads.…”