Effect of a multicomponent intervention on achievement and improvements in quality-of-care indices among people with Type 2 diabetes in South Asia: the CARRS trial

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 (HbA_1c <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 HbA_1c , ≥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 HbA_1c , 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.

Isolated HbA1c identifies a different subgroup of individuals with type 2 diabetes compared to fasting or post-challenge glucose in Asian Indians: The CARRS and MASALA studies

AIMS

Guidelines recommend hemoglobin A1c (HbA1c) as a diagnostic test for type 2 diabetes, but its accuracy may differ in certain ethnic groups.

METHODS

The prevalence of type 2 diabetes by HbA1c, fasting glucose, and 2 h glucose was compared in 3016 participants from Chennai and Delhi, India from the CARRS-2 Study to 757 Indians in the U.S. from the MASALA Study. Type 2 diabetes was defined as fasting glucose ≥ 7.0 mmol/L, 2-h glucose ≥ 11.1 mmol/L, or HbA1c ≥ 6.5%. Isolated HbA1c diabetes was defined as HbA1c ≥ 6.5% with fasting glucose < 7.0 mmol/L and 2 h glucose < 11.1 mmol/L.

RESULTS

The age, sex, and BMI adjusted prevalence of diabetes by isolated HbA1c was 2.9% (95% CI: 2.2-4.0), 3.1% (95% CI: 2.3-4.1), and 0.8% (95% CI: 0.4-1.8) in CARRS-Chennai, CARRS-Delhi, and MASALA, respectively. The proportion of diabetes diagnosed by isolated HbA1c was 19.4%, 26.8%, and 10.8% in CARRS-Chennai, CARRS-Delhi, and MASALA respectively. In CARRS-2, individuals with type 2 diabetes by isolated HbA1c milder cardio-metabolic risk than those diagnosed by fasting or 2-h measures.

CONCLUSIONS

In Asian Indians, the use of HbA1c for type 2 diabetes diagnosis could result in a higher prevalence. HbA1c may identify a subset of individuals with milder glucose intolerance.

Acceptability of a decision-support electronic health record system and its impact on diabetes care goals in South Asia: a mixed-methods evaluation of the CARRS trial

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 HbA_1c , 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 HbA_1c [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.

Feasibility and Results of Cancer Registry and Noncommunicable Disease Cohort Data Linkages in India

Background: Cancer registries worldwide are vital to determine cancer burden, plan cancer control measures, and facilitate research. Population-based cancer registries are a priority for LMICs by the UICC; the National Cancer Registry Program (NCRP) of India oversees 28 such registries. A primary function of registries is to combine data for the same individual from multiple sources. For other disease cohorts where cancer is an outcome of interest, registries can potentially connect information by linking datasets together. Barriers to successful registration and linkages include systems in which cancer is not a notifiable disease, no universal unique individual identifier exists, and lack of trained personnel. This study utilizes technology and infrastructure to develop better linkages, surveillance, and outcomes. Aim: To assess the feasibility of linking large cohorts designed for cardio-metabolic disease research with cancer registries in New Delhi and Chennai; determine additional steps required for linkage accuracy and completeness; and develop detailed protocols for future applications. Methods: A pilot protocol for linkage between a large diabetes cohort and cancer registries in Delhi and Chennai was developed using MatchPro, a probabilistic record linkage program developed for cancer registries. Probabilistic software links datasets together in the presence of uncertainty (eg misspelled or abbreviated names) to identify record pairs with high probability of representing the same individual. For this study, algorithms were developed to address unique aspects of names and demographics in India. The software and algorithms focused on: detecting duplicates in cancer registries; and linking registries with external files from diabetes cohorts. In Delhi, 3 1-year datasets covering 3 years (2010, 2011, 2012) were linked with the diabetes cohort; in Chennai, the linkage included 3 5-year datasets covering 15 years (2000-04, '05-'09, '10-'14). The unique ID (Aadhaar) is not collected or linked systematically between different systems at this point in time. Results: Linkage attempts yielded potential matches ranked according to probabilistic scores; highest scores were reviewed to determine true matches. In Chennai, this process yielded: (2010-2014) 21% self-reported (SR) cases matching perfectly, 36% requiring follow-up, 13 nonreported (NR) cases found; 2005-2009: 33% SR cases matched perfectly, 1 NR case found; 2000-2004: 1 NR case. Also, 2 training workshops on data linkages and software were held. Conclusion: Linkages between cancer registries and other data sources are feasible in LMICs using probabilistic record linkage software augmented by manual matching. Future efforts to use existing epidemiologic resources (cohorts) and cancer research infrastructure (registries and clinical centers) can enhance research including understanding shared risk factors and pathophysiologic mechanisms e.g., between cancer and other NCD.

A case-control study on insulin resistance, metabolic co-variates & prediction score in non-alcoholic fatty liver disease

BACKGROUND & OBJECTIVE

Asian Indians have a high prevalence of insulin resistance and the metabolic syndrome. Currently, non-alcoholic fatty liver disease (NAFLD) is considered to be an integral part of the metabolic syndrome with insulin resistance as a central pathogenic factor. We studied anthropometric parameters, insulin resistance and metabolic co-variates in subjects with NAFLD as compared to those without NAFLD, and also developed a prediction score for NAFLD.

METHODS

Thirty nine subjects with NAFLD and 82 controls were selected for the study after ultrasonography of 121 consecutive apparently healthy subjects. Anthropometric profile [body mass index (BMI), waist circumference (WC) etc,], lipid profile, hepatic aminotransferases, fasting blood glucose (FBG), insulin were recorded and value of homeostasis model assessment of insulin resistance (HOMA-IR) was analysed. Step-wise logistic regression analysis and area under the receiver operator curve (aROC) were analysed to arrive at a prediction score.

RESULTS

Overall, prevalence of NAFLD was 32.2 per cent and prevalence of metabolic syndrome was seen in 41 per cent among cases and 19.5 per cent in controls (P<0.01). Subjects with NAFLD had significantly higher values of BMI, WC, hip circumference, FBG, fasting insulin, total cholesterol and serum triglycerides. Step-wise logistic regression analysis showed odds ratio (OR) and 95 per cent confidence interval (CI) for BMI [ 4.3 (1.6, 11.3)], FBG [5.5 (1.5, 19.8)] and fasting insulin [ 2.4 (1.0, 5.8)] as independent predictors of NAFLD. The prediction score for NAFLD was; 1 (fasting insulin) +1.6 (BMI) + 1.9 (FBG) (sensitivity of 84.6%, specificity of 51.2% and aROC 76%).

INTERPRETATION & CONCLUSION

In this study, presence of NAFLD indicated close relationship with multiple features of metabolic syndrome. The prediction score developed could be used as a screening tool to predict NAFLD among Asian Indians in north India.