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Odisha has 4.2 million diabetic patients against the country's 70 million with an urban prevalence of nearly 15.4%. Diabetes is affecting younger age groups, thus having a crucial impact on quality of life of the affected. A qualitative endeavour was attempted at the diabetic clinic of a tertiary care set up in the capital city of Bhubaneswar to create a diabetic surveillance data assembly, wherein subjects above 18 years of age and newly diagnosed or on follow-up, after obtaining informed consent, were made to respond to a quality of life (QOLID) validated tool. The pretested tool has 8-domain role limitation due to physical health, physical endurance, general health, treatment satisfaction, symptom botherness, financial worries, emotional/mental health, and diet advice tolerance. The validated tool had 34 items (questions) that were selected to represent these domains on the basis of extraction communality, factor loading, and interitem and item-total correlations. The final questionnaire had an overall Cronbach's alpha value of 0.894 (subscale: 0.55 to 0.85), showing high internal consistency in the current study population. A score for each domain was calculated by simple addition of items scores. Each individual domain score was then standardized by dividing by maximum possible domain score and multiplying by 100. All individual standardized domain scores were then added and divided by 8 (number of domain) to obtain an overall score. The data collection was done for 400 patients as an interim analysis. Univariate and subsequently multivariate analysis was performed to decide the predictors that affected quality of life. Age over 50 years (OR = 1.81, CI 1.12-2.93; p =0.014), female gender (OR = 2.05, CI 1.26-3.35; p =0.004), having foot complications (OR = 2.81, CI 1.73-4.55; p < 0.001), and having depression (OR = 1.88, CI 1.15-3.06, p =0.011) emerged as predictors of poor QOLID scores. The tool can be made a subtle part of chronic case management of diabetes to ensure patient's participation in the treatment of the disease and to create a database that can redefine diabetic care in India to suit the diverse regional settings in the country., Competing Interests: The authors do not have any conflicts of interest., (Copyright © 2020 Dayanidhi Meher et al.)

Odisha has 4.2 million diabetic patients against the country's 70 million with an urban prevalence of nearly 15.4%. Diabetes is affecting younger age groups, thus having a crucial impact on quality of life of the affected. A qualitative endeavour was attempted at the diabetic clinic of a tertiary care set up in the capital city of Bhubaneswar to create a diabetic surveillance data assembly, wherein subjects above 18 years of age and newly diagnosed or on follow-up, after obtaining informed consent, were made to respond to a quality of life (QOLID) validated tool. The pretested tool has 8-domain role limitation due to physical health, physical endurance, general health, treatment satisfaction, symptom botherness, financial worries, emotional/mental health, and diet advice tolerance. The validated tool had 34 items (questions) that were selected to represent these domains on the basis of extraction communality, factor loading, and interitem and item-total correlations. The final questionnaire had an overall Cronbach's alpha value of 0.894 (subscale: 0.55 to 0.85), showing high internal consistency in the current study population. A score for each domain was calculated by simple addition of items scores. Each individual domain score was then standardized by dividing by maximum possible domain score and multiplying by 100. All individual standardized domain scores were then added and divided by 8 (number of domain) to obtain an overall score. The data collection was done for 400 patients as an interim analysis. Univariate and subsequently multivariate analysis was performed to decide the predictors that affected quality of life. Age over 50 years (OR = 1.81, CI 1.12-2.93; p=0.014), female gender (OR = 2.05, CI 1.26-3.35; p=0.004), having foot complications (OR = 2.81, CI 1.73-4.55; p < 0.001), and having depression (OR = 1.88, CI 1.15-3.06, p=0.011) emerged as predictors of poor QOLID scores. The tool can be made a subtle part of chronic case management of diabetes to ensure patient's participation in the treatment of the disease and to create a database that can redefine diabetic care in India to suit the diverse regional settings in the country. [ABSTRACT FROM AUTHOR]

Background: Quality in diabetic management is the need of the hour, in eye of the menacing increase in the disease in India. Hence, a sensitive qualitative handling of outpatient visits is warranted and an inbuilt mechanism of Quality of life scales (which are proxy of the patient's response to disease) and Diabetic care scales (proxy for patient's satisfaction to the care extended), would offer supportive evidence to physicians, of areas where they will have to be more careful. Aims and Objectives: To assess the Diabetic Care scale (DCS) for the subjects seeking management from the diabetic care unit. To find out the factors associated with the DCS and derive inferences to improve upon quality of management in the given sample Methodology: Diabetics were made to answer to Quality of Life in Diabetics (QOLID) and Diabetic Care Scale (DCS), validated and pretested for Indian populations; and factors affecting patient's responses were ascertained, to improve care. Final sample of 599 interviews were assessed. To identify the predictors of diabetic care, diabetic care scale was dichotomized on the basis of its median value. Results: QOLID domains were inversely correlated with DCS, strongly significant (treatment satisfaction, general health, symptom botherness, financial worries, emotional health and physical endurance). Role limitations to physical health were also positively related to DCS (-0.422; p<0.001), which indicated that this domain affected DCS positively and significantly. Overall QOLID and DCS scores were negatively correlated and significant (-0.650; p<0.005). Education (UOR 0.76; SD 0.64 - 0.90, p=0.002), treatment, medical adherence in diabetics about being careless with medications (AOR=2.38 SD 1.50 - 3.77, <0.001) emerged predictors of poor DCS scores. DCS can be used as a prelim screening to evaluate the quality of care in diabetic management in early stages so as to rectify any gaps and improve through specialized counselling in subsequent visits. Wide use of these tools is recommended, both in rural and urban scenario to improve and control the diabetic epidemic in India. [ABSTRACT FROM AUTHOR]

Objective:The EAACI/GA2LEN/EDF/WAO recommendation of increasing antihistamines’ dose up to four times in urticaria not adequately controlled with the standard dose is largely based on expert opinion. The objective of this study is to test the current urticaria guidelines of up-dosing antihistamines as second-line treatment. Methods:This was an open-label study conducted prospectively on 113 patients with chronic spontaneous urticaria. All patients were treated with sequentially increasing doses of levocetrizine (5 mg, 10 mg, 15 mg and 20 mg/day) every week till the patients became completely asymptomatic or dose of 20 mg/day reached. Urticaria Activity Score (UAS)-7, urticaria-related quality-of-life (CU-Q2oL) and patients’ global assessment were used to assess treatment response. Results:Twenty-one (18.58%) patients became asymptomatic with levocetirizine 5 mg/day, while 50 required higher doses of levocetirizine for complete control: 29/92 (31.52%), 6/63 (9.52%) and 15/57 (26.31%) with 10 mg, 15 mg and 20 mg/day, respectively. The percentage of patients experiencing >75% improvement increased with increasing doses of levocetirizine: 26.54%, 53.98%, 60.17% and 69.91% with 5 mg, 10 mg, 15 mg and 20 mg/day, respectively. Sequential up-dosing of levocetirizine produced a progressive improvement in both urticaria control (UAS-7) and quality-of-life (CU-Q2oL) without significantly increasing somnolence. Conclusions:Our results support the current recommendations of increasing antihistamines up to four times the standard dose in patients who fail the first-line treatment. [ABSTRACT FROM PUBLISHER]