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1.
Diabet Med ; 37(11): 1825-1831, 2020 11.
Article in English | MEDLINE | ID: mdl-31479537

ABSTRACT

AIMS: To evaluate whether and what combinations of diabetes quality metrics were achieved in a multicentre trial in South Asia evaluating a multicomponent quality improvement intervention that included non-physician care coordinators to promote adherence and clinical decision-support software to enhance physician practices, in comparision with usual care. METHODS: Using data from the Centre for Cardiometabolic Risk Reduction in South Asia (CARRS) trial, we evaluated the proportions of trial participants achieving specific and combinations of five diabetes care targets (HbA1c <53 mmol/mol [7%], blood pressure <130/80 mmHg, LDL cholesterol <2.6 mmol/L, non-smoking status, and aspirin use). Additionally, we examined the proportions of participants achieving the following risk factor improvements from baseline: ≥11-mmol/mol (1%) reduction in HbA1c , ≥10-mmHg reduction in systolic blood pressure, and/or ≥0.26-mmol/l reduction in LDL cholesterol. RESULTS: Baseline characteristics were similar in the intervention and usual care arms. Overall, 12.3%, 29.4%, 36.5%, 19.5% and 2.2% of participants in the intervention group and 16.2%, 38.3%, 31.6%, 11.3% and 0.8% of participants in the usual care group achieved any one, two, three, four or five targets, respectively. We noted sizeable improvements in HbA1c , blood pressure and cholesterol, and found that participants in the intervention group were twice as likely to achieve improvements in all three indices at 12 months that were sustained over 28 months of the study [relative risk 2.1 (95% CI 1.5,2.8) and 1.8 (95% CI 1.5,2.3), respectively]. CONCLUSIONS: The intervention was associated with significantly higher achievement of and greater improvements in composite diabetes quality care goals. However, among these higher-risk participants, very small proportions achieved the complete group of targets, which suggests that achievement of multiple quality-of-care goals is challenging and that other methods may be needed in closing care gaps.


Subject(s)
Decision Support Systems, Clinical , Diabetes Mellitus, Type 2/therapy , Quality Improvement , Quality Indicators, Health Care , Aspirin/therapeutic use , Blood Pressure , Cholesterol, LDL/metabolism , Delivery of Health Care/organization & administration , Diabetes Mellitus, Type 2/metabolism , Glycated Hemoglobin/metabolism , Humans , India , Pakistan , Platelet Aggregation Inhibitors/therapeutic use , Quality of Health Care , Smoking/epidemiology
2.
Diabet Med ; 35(12): 1644-1654, 2018 12.
Article in English | MEDLINE | ID: mdl-30142228

ABSTRACT

AIMS: To describe physicians' acceptance of decision-support electronic health record system and its impact on diabetes care goals among people with Type 2 diabetes. METHODS: We analysed data from participants in the Centre for Cardiometabolic Risk Reduction in South Asia (CARRS) trial, who received the study intervention (care coordinators and use of a decision-support electronic health record system; n=575) using generalized estimating equations to estimate the association between acceptance/rejection of decision-support system prompts and outcomes (mean changes in HbA1c , blood pressure and LDL cholesterol) considering repeated measures across all time points available. We conducted in-depth interviews with physicians to understand the benefits, challenges and value of the decision-support electronic health record system and analysed physicians' interviews using Rogers' diffusion of innovation theory. RESULTS: At end-of-trial, participants with diabetes for whom glycaemic, systolic blood pressure, diastolic blood pressure and LDL cholesterol decision-support electronic health record prompts were accepted vs rejected, experienced no reduction in HbA1c [mean difference: -0.05 mmol/mol (95% CI -0.22, 0.13); P=0.599], but statistically significant improvements were observed for systolic blood pressure [mean difference: -11.6 mmHg (95% CI -13.9, -9.3); P ≤ 0.001], diastolic blood pressure [mean difference: -5.2 mmHg (95% CI -6.5, -3.8); P ≤ 0.001] and LDL cholesterol [mean difference: -0.7 mmol/l (95% CI -0.6, -0.8); P ≤0.001], respectively. The relative advantages and compatibility of the decision-support electronic health record system with existing clinic set-ups influenced physicians' acceptance of it. Software complexities and data entry challenges could be overcome by task-sharing. CONCLUSION: Wider adherence to decision-support electronic health record prompts could potentially improve diabetes goal achievement, particularly when accompanied by assistance from a non-physician health worker.


Subject(s)
Clinical Trials as Topic , Decision Support Systems, Clinical , Electronic Health Records , Guideline Adherence/statistics & numerical data , Patient Care Planning , Physicians , Adult , Asia/epidemiology , Attitude of Health Personnel , Clinical Trials as Topic/methods , Clinical Trials as Topic/organization & administration , Decision Making , Decision Support Systems, Clinical/organization & administration , Diabetes Mellitus, Type 2/complications , Diabetes Mellitus, Type 2/therapy , Diabetic Angiopathies/prevention & control , Electronic Health Records/organization & administration , Female , Humans , Male , Middle Aged , Patient Care Planning/organization & administration , Physicians/psychology , Physicians/statistics & numerical data , Primary Health Care/methods , Primary Health Care/organization & administration , Primary Health Care/statistics & numerical data , Risk Reduction Behavior
3.
PLoS One ; 6(6): e20821, 2011.
Article in English | MEDLINE | ID: mdl-21695127

ABSTRACT

OBJECTIVE: To estimate individual and household economic impact of cardiovascular disease (CVD) in selected low- and middle-income countries (LMIC). BACKGROUND: Empirical evidence on the microeconomic consequences of CVD in LMIC is scarce. METHODS AND FINDINGS: We surveyed 1,657 recently hospitalized CVD patients (66% male; mean age 55.8 years) from Argentina, China, India, and Tanzania to evaluate the microeconomic and functional/productivity impact of CVD hospitalization. Respondents were stratified into three income groups. Median out-of-pocket expenditures for CVD treatment over 15 month follow-up ranged from 354 international dollars (2007 INT$, Tanzania, low-income) to INT$2,917 (India, high-income). Catastrophic health spending (CHS) was present in >50% of respondents in China, India, and Tanzania. Distress financing (DF) and lost income were more common in low-income respondents. After adjustment, lack of health insurance was associated with CHS in Argentina (OR 4.73 [2.56, 8.76], India (OR 3.93 [2.23, 6.90], and Tanzania (OR 3.68 [1.86, 7.26] with a marginal association in China (OR 2.05 [0.82, 5.11]). These economic effects were accompanied by substantial decreases in individual functional health and productivity. CONCLUSIONS: Individuals in selected LMIC bear significant financial burdens following CVD hospitalization, yet with substantial variation across and within countries. Lack of insurance may drive much of the financial stress of CVD in LMIC patients and their families.


Subject(s)
Cardiovascular Diseases/economics , Hospitalization/economics , Income , Argentina , China , Cross-Sectional Studies , Demography , Female , Health Care Surveys , Health Expenditures , Humans , India , Logistic Models , Male , Middle Aged , Multivariate Analysis , Tanzania
4.
Diabet Med ; 25(10): 1187-94, 2008 Oct.
Article in English | MEDLINE | ID: mdl-19046197

ABSTRACT

AIM: To highlight the regional difference in the prevalence of diabetes mellitus (DM) and to explore determinants in variability in the Indian industrial population. METHODS: A cross-sectional survey was carried out among the employees and their family members (10 930 individuals, mean age 39.6 years, 6764 male) of eleven medium-to-large industries from diverse sites in India, using a stratified random sampling technique. Information on behavioural, clinical and biochemical risk factors of DM was obtained, through standardized instruments. DM was diagnosed when fasting blood glucose was > or = 7.0 mmol/l and/or individuals took drug treatment for DM. Multiple logistic regression analysis was carried out to identify the potential predictors of DM. RESULT: In the 20 to 69-year-old age group, the crude prevalence of DM and impaired fasting glucose was 10.1 and 5.3%, respectively. Urban sites had a higher prevalence and awareness of DM status. Individuals in the lower education group had a high prevalence of DM (11.6%). In diabetic subjects, 38.4% were unaware that they had diabetes. Waist-circumference-to-height ratio had a higher DM predictive power than waist circumference and body mass index. The risk factors associated with overall prevalence of DM were: age, sex, low-education level, family history of DM, hypertension and overweight/obesity. Interaction of risk factors was observed only in urban high-prevalence sites. CONCLUSION: There are wide regional variations in the prevalence of DM in India. The high burden of undetected diabetes, even in settings with universal access to on-site health care, highlights the need for innovative prevention and control strategies.


Subject(s)
Diabetes Mellitus/epidemiology , Industry , Urban Population , Adult , Age Factors , Aged , Body Height , Body Weight , Cross-Sectional Studies , Diabetes Mellitus/diagnosis , Educational Status , Female , Health Surveys , Humans , Hypertension/complications , Hypertension/epidemiology , India/epidemiology , Logistic Models , Male , Middle Aged , Prevalence , Risk Factors , Sex Factors , Waist Circumference , Young Adult
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