The WHO Global Diabetes Compact: a new initiative to support people living with diabetes

Hunt, Daniel; Hemmingsen, Bianca; Matzke, Alena; Varghese, Cherian; Hammerich, Asmus; Luciani, Silvana; Hennis, Anselm; Branca, Francesco; Bull, Fiona; Berdzuli, Nino; Dangou, J. M.; Pendse, Razia; Pratt, Angela; Mikkelsen, Bente

doi:10.1016/s2213-8587(21)00111-x

Cited by 43 publications

(24 citation statements)

References 5 publications

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“…The slow and limited adaptations of LMIC health systems to deal with the escalating prevalence of noncommunicable diseases (NCDs) underly this differential. Diabetes risk factors are increasing globally; however, diagnosis, screening, treatment, and disease management are unavailable or unaffordable in many LMICs [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Identifying the necessary capacities for the adaptation of a diabetes phenotyping algorithm in countries of differing economic development status

Jackson-Morris

Sembajwe

Mustapha

et al. 2023

Global Health Action

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Background In 2019, the World Health Organization recognised diabetes as a clinically and pathophysiologically heterogeneous set of related diseases. Little is currently known about the diabetes phenotypes in the population of low- and middle-income countries (LMICs), yet identifying their different risks and aetiology has great potential to guide the development of more effective, tailored prevention and treatment. Objectives This study reviewed the scope of diabetes datasets, health information ecosystems, and human resource capacity in four countries to assess whether a diabetes phenotyping algorithm (developed under a companion study) could be successfully applied. Methods The capacity assessment was undertaken with four countries: Trinidad, Malaysia, Kenya, and Rwanda. Diabetes programme staff completed a checklist of available diabetes data variables and then participated in semi-structured interviews about Health Information System (HIS) ecosystem conditions, diabetes programme context, and human resource needs. Descriptive analysis was undertaken. Results Only Malaysia collected the full set of the required diabetes data for the diabetes algorithm, although all countries did collect the required diabetes complication data. An HIS ecosystem existed in all settings, with variations in data hosting and sharing. All countries had access to HIS or ICT support, and epidemiologists or biostatisticians to support dataset preparation and algorithm application. Conclusions Malaysia was found to be most ready to apply the phenotyping algorithm. A fundamental impediment in the other settings was the absence of several core diabetes data variables. Additionally, if countries digitise diabetes data collection and centralise diabetes data hosting, this will simplify dataset preparation for algorithm application. These issues reflect common LMIC health systems’ weaknesses in relation to diabetes care, and specifically highlight the importance of investment in improving diabetes data, which can guide population-tailored prevention and management approaches.

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Section: Introductionmentioning

confidence: 99%

Identifying the necessary capacities for the adaptation of a diabetes phenotyping algorithm in countries of differing economic development status

Jackson-Morris

Sembajwe

Mustapha

et al. 2023

Global Health Action

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“…A retrospective cohort study in southern England using databases [ 38 ] showed that the rate of utilization of primary care services by people with type 2 diabetes increased from 2013 to 2020, but this increase did not correlate with better outcomes. The World Health Organization’s Global Diabetes Compact [ 39 ], a global initiative, proposed strengthening primary health care for accessible diabetes treatment. It is therefore timely to perform an updated systematic review and meta-analysis to enhance applicable knowledge for the management of type 2 diabetes in primary care.…”

Section: Introductionmentioning

confidence: 99%

Effectiveness of the chronic care model for adults with type 2 diabetes in primary care: a systematic review and meta-analysis

et al. 2022

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Background Mixed evidence exists regarding the effectiveness of the Chronic Care Model (CCM) with patient outcomes. The aim of this review is to examine the effectiveness of CCM interventions on hemoglobin A1c (HbA1c), systolic BP (SBP), diastolic BP (DBP), LDL cholesterol and body mass index (BMI) among primary care adults with type 2 diabetes. Methods PubMed, Embase, CINAHL, Cochrane Central Registry of Controlled Trials, Scopus and Web of Science were searched from January 1990 to June 2021 for randomized controlled trials (RCTs) comparing CCM interventions against usual care among adults with type 2 diabetes mellitus in primary care with HbA1c, SBP, DBP, LDL cholesterol and BMI as outcomes. An abbreviated search was performed from 2021 to April 2022. This study followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines for data extraction and Cochrane risk of bias assessment. Two reviewers independently extracted the data. Meta-analysis was performed using Review Manager software. Heterogeneity was evaluated using χ2 and I2 test statistics. Overall effects were evaluated using Z statistic. Results A total of 17 studies involving 16485 patients were identified. Most studies had low risks of bias. Meta-analysis of all 17 studies revealed that CCM interventions significantly decreased HbA1c levels compared to usual care, with a mean difference (MD) of −0.21%, 95% CI −0.30, −0.13; Z = 5.07, p<0.00001. Larger effects were experienced among adults with baseline HbA1c ≥8% (MD −0.36%, 95% CI −0.51, −0.21; Z = 5.05, p<0.00001) and when four or more CCM elements were present in the interventions (MD −0.25%, 95% CI −0.35, −0.15; Z = 4.85, p<0.00001). Interventions with CCM decreased SBP (MD −2.93 mmHg, 95% CI −4.46, −1.40, Z = 3.75, p=0.0002) and DBP (MD −1.35 mmHg, 95% CI −2.05, −0.65, Z = 3.79, p=0.0002) compared to usual care but there was no impact on LDL cholesterol levels or BMI. Conclusions CCM interventions, compared to usual care, improve glycaemic control among adults with type 2 diabetes in primary care, with greater reductions when the mean baseline HbA1c is ≥8% and with interventions containing four or more CCM elements. Systematic review registration PROSPERO CRD42021273959

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“…The United Nations (UN) Sustainable Development Goal (SDG) target 3.4 is to reduce premature mortality owing to non-communicable diseases (NCDs) by one-third, which cannot be achieved without prevention and control of diabetes 9,10 . The World Health Organization (WHO) launched the Global Diabetes Compact in 2021, a global initiative aimed at sustained improvements in diabetes prevention and care with special focus for people living in LMCs 11,12 .…”

Section: Introductionmentioning

confidence: 99%

Socioeconomic Inequality in Awareness, Treatment and Control of Diabetes Among Adults in India: Evidence from National Family Health Survey of India (NFHS), 2019-2021

Maiti

Akhtar

Upadhyay

et al. 2022

Preprint

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Diabetes is a growing epidemic and a major threat to most of the households in India. There is little evidence on awareness, treatment, and control (ATC) among adults in India is limited. We estimate the prevalence and ATC of diabetes among adults across various sociodemographic groups and states of India. We used 2,078,315 individuals aged 15 years and over from the recent fifth round of National Family Health Survey (NFHS-5), 2019-21, that was carried out across all states of India. Diabetes individuals were identified as those who had random blood glucose above 140 mg/dL or taking diabetes medication or doctor diagnosed diabetes. Individuals who were measured as diabetic and (i) reported diagnosis were labelled as aware, (ii) reported taking medication for controlling blood glucose levels as treated and (iii) had measured blood glucose levels < 140 mg/dL as controlled. The estimates of diabetes prevalence, and ATC were age-sex adjusted, and disaggregated by household wealth quintile, education, age, sex, urban-rural, caste, religion, marital status, household size and state. Concentration indices was used to quantify socioeconomic inequalities and multivariable logistic regression was used to estimate adjusted differences in these outcomes. We estimated diabetes prevalence at 16.1% (15.9–16.1%). Among those with diabetes, 27.5% (27.1–27.9%) were aware, 21.5% (21.1–21.7%) were treated and 7% (6.8–7.1%) were under control. Across states of India, adjusted rates of ATC varied from 14.4% (12.1–16.8%) to 54.4% (40.3–68.4%), from 9.3% (7.5–11.1%) to 41.2% (39.9–42.6%), and from 2.7% (1.6–3.7%) to 11.9% (9.7–14.0%), respectively. Age–sex adjusted rates were lower (p < 0.001) in poorer, less educated, and social backward groups, as well as for male and residents of rural areas. Among individuals with diabetes, the richest fifth were 12.4 percentage points (pp) (11.3–13.4; p < 0.001), 10.5 pp (9.7–11.4; p < 0.001), and 2.3 pp (1.6-3.0; p < 0.001) more likely to be aware, getting treated, and having control, respectively, than the poorest fifth. The concentration index of ATC was 0.089 (0.085–0.092), 0.083 (0.079–0.085) and 0.017 (0.015–0.018) respectively. Overall, the ATC of diabetes is low in India. The ATC was much lower among adults belonging to poorer class and were less educated. Targeted intervention and management can reduce the diabetes burden in India.

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The WHO Global Diabetes Compact: a new initiative to support people living with diabetes

Cited by 43 publications

References 5 publications

Identifying the necessary capacities for the adaptation of a diabetes phenotyping algorithm in countries of differing economic development status

Identifying the necessary capacities for the adaptation of a diabetes phenotyping algorithm in countries of differing economic development status

Effectiveness of the chronic care model for adults with type 2 diabetes in primary care: a systematic review and meta-analysis

Socioeconomic Inequality in Awareness, Treatment and Control of Diabetes Among Adults in India: Evidence from National Family Health Survey of India (NFHS), 2019-2021

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