Infection and Foot Care in Diabetics Seeking Treatment in a Tertiary Care Hospital, Bhubaneswar, Odisha State, India

Diabetes mellitus is a major public health problem that can cause a number of serious complications. Foot ulceration is one of its most common complications. Poor foot care knowledge and practices are important risk factors for foot problems among diabetics. The present study was undertaken in the diabetes outpatient department of a tertiary care hospital to assess the practices regarding foot care in diabetes, find out the determinants of foot ulcer in diabetics, and offer suggestions to improve care. After informed consent, a total of 124 diabetics were interviewed to collect all relevant information. The diabetic foot care practice responses were converted into scores and for the sake of analysis were inferred as poor (0–5), fair (6-7), and good (>7) practices. Of the study population, 68.5% (85/124) consisted of men. The disease was diagnosed within the last 5 years for 66% (81/124) of the study participants. Of the study subjects, 83% (103/124) were on oral hypoglycemic agents (OHAs), 15.3% (19) on insulin, and 2 on diet control only. Among them about 18.5% had a history of foot ulcer. 37.9% reported using special slippers, 12% diabetics used slippers indoors, and 66.9% used slippers while using toilet. Of the study subjects, 67.8% said that feet should be inspected daily. 27.4% said they regularly applied oil/moisturizer on their feet. There is a need on part of the primary or secondary physician and an active participation of the patient to receive education about foot care as well as awareness regarding risk factors, recognition, clinical evaluation, and thus prevention of the complications of diabetes

Compliance with the treatment of diabetes mellitus and the factors associated

Background: The prevalence of diabetes mellitus is growing rapidly worldwide and is reaching epidemic proportions. Epidemiological data indicate that all nations, rich and poor, are suffering from the impact of the diabetes epidemic. Effective care of diabetes with pharmacological and non-pharmacological methods is required, but it is impossible to control diabetes and its complications, as well as mortality, without strong compliance or adherence to therapy. Aims & Objectives to study the compliance rate of the patients with type 1 & 2 diabetes to the prescribed medications and to find out its association with different socio-demographic factors and other patient characteristics affecting compliance. Methodology: A cross sectional observational study was done using a pre-designed, semi-structured, and pre-tested questionnaire. Patients' clinical and socio-demographic data, were obtained. The Morisky medication adherence scale (MMAS-8) was used to assess adherence to prescribed medications. Results: Overall compliance was very low (6%) and it was associated with education, Rural or urban dwellings, female gender and lower socio economic class. High compliance was associated with better glycaemic control.&nbsp

Diabetes care scale: a first line screening of self-care and treatment behavior in diabetics seeking treatment at a tertiary care setting in Bhubaneswar, Odisha

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

Quality of Life Assessment in Diabetic Patients Using a Validated Tool in a Patient Population Visiting a Tertiary Care Center in Bhubaneswar, Odisha, India

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