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Summary Background The prevalence of overweight, obesity, and diabetes is rising rapidly in low-income and middle-income countries (LMICs), but there are scant empirical data on the association between body-mass index (BMI) and diabetes in these settings. Methods In this cross-sectional study, we pooled individual-level data from nationally representative surveys across 57 LMICs. We identified all countries in which a WHO Stepwise Approach to Surveillance (STEPS) survey had been done during a year in which the country fell into an eligible World Bank income group category. For LMICs that did not have a STEPS survey, did not have valid contact information, or declined our request for data, we did a systematic search for survey datasets. Eligible surveys were done during or after 2008; had individual-level data; were done in a low-income, lower-middle-income, or upper-middle-income country; were nationally representative; had a response rate of 50% or higher; contained a diabetes biomarker (either a blood glucose measurement or glycated haemoglobin [HbA1c]); and contained data on height and weight. Diabetes was defined biologically as a fasting plasma glucose concentration of 7·0 mmol/L (126·0 mg/dL) or higher; a random plasma glucose concentration of 11·1 mmol/L (200·0 mg/dL) or higher; or a HbA1c of 6·5% (48·0 mmol/mol) or higher, or by self-reported use of diabetes medication. We included individuals aged 25 years or older with complete data on diabetes status, BMI (defined as normal [18·5–22·9 kg/m2], upper-normal [23·0–24·9 kg/m2], overweight [25·0–29·9 kg/m2], or obese [≥30·0 kg/m2]), sex, and age. Countries were categorised into six geographical regions: Latin America and the Caribbean, Europe and central Asia, east, south, and southeast Asia, sub-Saharan Africa, Middle East and north Africa, and Oceania. We estimated the association between BMI and diabetes risk by multivariable Poisson regression and receiver operating curve analyses, stratified by sex and geographical region. Findings Our pooled dataset from 58 nationally representative surveys in 57 LMICs included 685 616 individuals. The overall prevalence of overweight was 27·2% (95% CI 26·6–27·8), of obesity was 21·0% (19·6–22·5), and of diabetes was 9·3% (8·4–10·2). In the pooled analysis, a higher risk of diabetes was observed at a BMI of 23 kg/m2 or higher, with a 43% greater risk of diabetes for men and a 41% greater risk for women compared with a BMI of 18·5–22·9 kg/m2. Diabetes risk also increased steeply in individuals aged 35–44 years and in men aged 25–34 years in sub-Saharan Africa. In the stratified analyses, there was considerable regional variability in this association. Optimal BMI thresholds for diabetes screening ranged from 23·8 kg/m2 among men in east, south, and southeast Asia to 28·3 kg/m2 among women in the Middle East and north Africa and in Latin America and the Caribbean. Interpretation The association between BMI and diabetes risk in LMICs is subject to substantial regional variability. Diabetes risk is greater at lower BMI thresholds and at younger ages than reflected in currently used BMI cutoffs for assessing diabetes risk. These findings offer an important insight to inform context-specific diabetes screening guidelines. Funding Harvard T H Chan School of Public Health McLennan Fund: Dean's Challenge Grant Program.

Summary Background Approximately 80% of the 463 million adults worldwide with diabetes live in low-income and middle-income countries (LMICs). A major obstacle to designing evidence-based policies to improve diabetes outcomes in LMICs is the scarce availability of nationally representative data on the current patterns of treatment coverage. The objectives of this study were to estimate the proportion of adults with diabetes in LMICs who receive coverage of recommended pharmacological and non-pharmacological diabetes treatment; and to describe country-level and individual-level characteristics that are associated with treatment. Methods We did a cross-sectional analysis of pooled, individual data from 55 nationally representative surveys in LMICs. Our primary outcome of self-reported diabetes treatment coverage was based on population-level monitoring indicators recommended in the 2020 WHO Package of Essential Noncommunicable Disease Interventions. Surveys were included if they were done in 2008 or after in an LMIC, as classified by the World Bank in the year the survey was done; were nationally representative; had individual-level data; contained a diabetes biomarker (fasting glucose, random glucose, or glycated haemoglobin); and had data on one or more diabetes treatments. Our sample included non-pregnant individuals with an available diabetes biomarker who were at least 25 years of age. We assessed coverage of three pharmacological and three non-pharmacological treatments among people with diabetes. At the country level, we estimated the proportion of individuals reporting coverage by per-capita gross national income and geographical region. At the individual level, we used logistic regression models to assess coverage along several key individual characteristics including sex, age, body-mass index, wealth quintile, and educational attainment. In the primary analysis, we scaled sample weights such that countries were weighted equally. Findings The final pooled sample from the 55 LMICs included 680 102 total individuals and 37 094 individuals with diabetes. Using equal weights for each country, diabetes prevalence was 9·0% (95% CI 8·7–9·4), with 43·9% (41·9–45·9) reporting a previous diabetes diagnosis. Overall, 4·6% (3·9–5·4) of individuals with diabetes self-reported meeting need for all treatments recommended for them. Coverage of glucose-lowering medication was 50·5% (48·6–52·5); antihypertensive medication was 41·3% (39·3–43·3); cholesterol-lowering medication was 6·3% (5·5–7·2); diet counselling was 32·2% (30·7–33·7); exercise counselling was 28·2% (26·6–29·8); and weight-loss counselling was 31·5% (29·3–33·7). Countries at higher-income levels tended to have greater coverage. Female sex and higher age, body-mass index, educational attainment, and household wealth were also associated with greater coverage. Interpretation Fewer than one in ten people with diabetes in LMICs receive coverage of guideline-based comprehensive diabetes treatment. Scaling up the capacity of health systems to deliver treatment not only to lower glucose but also to address cardiovascular disease risk factors, such as hypertension and high cholesterol, are urgent global diabetes priorities. Funding National Clinician Scholars Program at the University of Michigan Institute for Healthcare Policy & Innovation, National Institute of Diabetes and Digestive and Kidney Diseases, Harvard Catalyst, and National Center for Advancing Translational Sciences of the National Institutes of Health.

Background Global cardiovascular disease (CVD) burden is high and rising, especially in low-income and middle-income countries (LMICs). Focussing on 45 LMICs, we aimed to determine (1) the adult population’s median 10-year predicted CVD risk, including its variation within countries by socio-demographic characteristics, and (2) the prevalence of self-reported blood pressure (BP) medication use among those with and without an indication for such medication as per World Health Organization (WHO) guidelines. Methods and findings We conducted a cross-sectional analysis of nationally representative household surveys from 45 LMICs carried out between 2005 and 2017, with 32 surveys being WHO Stepwise Approach to Surveillance (STEPS) surveys. Country-specific median 10-year CVD risk was calculated using the 2019 WHO CVD Risk Chart Working Group non-laboratory-based equations. BP medication indications were based on the WHO Package of Essential Noncommunicable Disease Interventions guidelines. Regression models examined associations between CVD risk, BP medication use, and socio-demographic characteristics. Our complete case analysis included 600,484 adults from 45 countries. Median 10-year CVD risk (interquartile range [IQR]) for males and females was 2.7% (2.3%–4.2%) and 1.6% (1.3%–2.1%), respectively, with estimates indicating the lowest risk in sub-Saharan Africa and highest in Europe and the Eastern Mediterranean. Higher educational attainment and current employment were associated with lower CVD risk in most countries. Of those indicated for BP medication, the median (IQR) percentage taking medication was 24.2% (15.4%–37.2%) for males and 41.6% (23.9%–53.8%) for females. Conversely, a median (IQR) 47.1% (36.1%–58.6%) of all people taking a BP medication were not indicated for such based on CVD risk status. There was no association between BP medication use and socio-demographic characteristics in most of the 45 study countries. Study limitations include variation in country survey methods, most notably the sample age range and year of data collection, insufficient data to use the laboratory-based CVD risk equations, and an inability to determine past history of a CVD diagnosis. Conclusions This study found underuse of guideline-indicated BP medication in people with elevated CVD risk and overuse by people with lower CVD risk. Country-specific targeted policies are needed to help improve the identification and management of those at highest CVD risk., Author summary Why was this study done? CVD burden in low-income and middle-income countries (LMICs) is high and rising. CVD risk estimation using validated risk prediction equations is recommended in most guidelines; however, there are few population-representative analyses of CVD risk and its association with socio-demographic characteristics. Despite guidelines recommending using CVD risk estimates as an essential first step in guiding management practices, the extent to which risk-based approaches are being implemented in LMICs is not well characterised. What did the researchers do and find? We analysed population-representative survey data from 45 LMICs to determine country-specific levels of CVD risk, associations between socio-demographic factors and levels of CVD risk, and adherence to WHO guidelines on use of blood pressure medication. We found high variation in CVD risk profiles, with higher levels of risk in the Europe and the Eastern Mediterranean region and lower levels of risk in sub-Saharan Africa, as well as an inverse association between CVD risk and higher education and employment in most countries. We found an underuse of medicines in people at elevated CVD risk across all countries (only 24.2% of males and 41.6% of females at high CVD risk are taking guideline-recommended BP medication) and an overuse of medicines in people at lower levels of CVD risk, with 47% of all BP medication being used by people at low CVD risk without a guideline indication for use. What do these findings mean? There is large variation in CVD risk across LMICs, and an inverse association between CVD risk and higher education and employment in most countries. There is an overuse of medicines in people at lower levels of CVD risk and an underuse of medicines in people at elevated CVD risk across all countries. The large heterogeneity of the findings in this study reflects varying country contexts. Country-specific targeted policies are needed to improve the identification and management of those at highest CVD risk.

OBJECTIVE Diabetes is a rapidly growing health problem in low- and middle-income countries (LMICs), but empirical data on its prevalence and relationship to socioeconomic status are scarce. We estimated diabetes prevalence and the subset with undiagnosed diabetes in 29 LMICs and evaluated the relationship of education, household wealth, and BMI with diabetes risk. RESEARCH DESIGN AND METHODS We pooled individual-level data from 29 nationally representative surveys conducted between 2008 and 2016, totaling 588,574 participants aged ≥25 years. Diabetes prevalence and the subset with undiagnosed diabetes was calculated overall and by country, World Bank income group (WBIG), and geographic region. Multivariable Poisson regression models were used to estimate relative risk (RR). RESULTS Overall, prevalence of diabetes in 29 LMICs was 7.5% (95% CI 7.1–8.0) and of undiagnosed diabetes 4.9% (4.6–5.3). Diabetes prevalence increased with increasing WBIG: countries with low-income economies (LICs) 6.7% (5.5–8.1), lower-middle-income economies (LMIs) 7.1% (6.6–7.6), and upper-middle-income economies (UMIs) 8.2% (7.5–9.0). Compared with no formal education, greater educational attainment was associated with an increased risk of diabetes across WBIGs, after adjusting for BMI (LICs RR 1.47 [95% CI 1.22–1.78], LMIs 1.14 [1.06–1.23], and UMIs 1.28 [1.02–1.61]). CONCLUSIONS Among 29 LMICs, diabetes prevalence was substantial and increased with increasing WBIG. In contrast to the association seen in high-income countries, diabetes risk was highest among those with greater educational attainment, independent of BMI. LMICs included in this analysis may be at an advanced stage in the nutrition transition but with no reversal in the socioeconomic gradient of diabetes risk.

Background Cardiovascular diseases are leading causes of death, globally, and health systems that deliver quality clinical care are needed to manage an increasing number of people with risk factors for these diseases. Indicators of preparedness of countries to manage cardiovascular disease risk factors (CVDRFs) are regularly collected by ministries of health and global health agencies. We aimed to assess whether these indicators are associated with patient receipt of quality clinical care. Methods and findings We did a secondary analysis of cross-sectional, nationally representative, individual-patient data from 187,552 people with hypertension (mean age 48.1 years, 53.5% female) living in 43 low- and middle-income countries (LMICs) and 40,795 people with diabetes (mean age 52.2 years, 57.7% female) living in 28 LMICs on progress through cascades of care (condition diagnosed, treated, or controlled) for diabetes or hypertension, to indicate outcomes of provision of quality clinical care. Data were extracted from national-level World Health Organization (WHO) Stepwise Approach to Surveillance (STEPS), or other similar household surveys, conducted between July 2005 and November 2016. We used mixed-effects logistic regression to estimate associations between each quality clinical care outcome and indicators of country development (gross domestic product [GDP] per capita or Human Development Index [HDI]); national capacity for the prevention and control of noncommunicable diseases (‘NCD readiness indicators’ from surveys done by WHO); health system finance (domestic government expenditure on health [as percentage of GDP], private, and out-of-pocket expenditure on health [both as percentage of current]); and health service readiness (number of physicians, nurses, or hospital beds per 1,000 people) and performance (neonatal mortality rate). All models were adjusted for individual-level predictors including age, sex, and education. In an exploratory analysis, we tested whether national-level data on facility preparedness for diabetes were positively associated with outcomes. Associations were inconsistent between indicators and quality clinical care outcomes. For hypertension, GDP and HDI were both positively associated with each outcome. Of the 33 relationships tested between NCD readiness indicators and outcomes, only two showed a significant positive association: presence of guidelines with being diagnosed (odds ratio [OR], 1.86 [95% CI 1.08–3.21], p = 0.03) and availability of funding with being controlled (OR, 2.26 [95% CI 1.09–4.69], p = 0.03). Hospital beds (OR, 1.14 [95% CI 1.02–1.27], p = 0.02), nurses/midwives (OR, 1.24 [95% CI 1.06–1.44], p = 0.006), and physicians (OR, 1.21 [95% CI 1.11–1.32], p < 0.001) per 1,000 people were positively associated with being diagnosed and, similarly, with being treated; and the number of physicians was additionally associated with being controlled (OR, 1.12 [95% CI 1.01–1.23], p = 0.03). For diabetes, no positive associations were seen between NCD readiness indicators and outcomes. There was no association between country development, health service finance, or health service performance and readiness indicators and any outcome, apart from GDP (OR, 1.70 [95% CI 1.12–2.59], p = 0.01), HDI (OR, 1.21 [95% CI 1.01–1.44], p = 0.04), and number of physicians per 1,000 people (OR, 1.28 [95% CI 1.09–1.51], p = 0.003), which were associated with being diagnosed. Six countries had data on cascades of care and nationwide-level data on facility preparedness. Of the 27 associations tested between facility preparedness indicators and outcomes, the only association that was significant was having metformin available, which was positively associated with treatment (OR, 1.35 [95% CI 1.01–1.81], p = 0.04). The main limitation was use of blood pressure measurement on a single occasion to diagnose hypertension and a single blood glucose measurement to diagnose diabetes. Conclusion In this study, we observed that indicators of country preparedness to deal with CVDRFs are poor proxies for quality clinical care received by patients for hypertension and diabetes. The major implication is that assessments of countries’ preparedness to manage CVDRFs should not rely on proxies; rather, it should involve direct assessment of quality clinical care., Author summary Why was the study done? Diseases such as high blood pressure and diabetes are becoming increasingly common in low- and middle-income countries (LMICs). Treatment for these conditions is simple and cheap. However, without treatment, sufferers are at high risk of adverse consequences, such as heart attacks and strokes. It is important therefore to be able to measure whether patients who need treatment are getting it. Currently, LMICs’ progress towards being able to treat patients with hypertension and diabetes is measured using proxies, for example, whether policies, guidelines, funding, structures, or human resources are in place. What did the researchers find? We measured whether 187,552 people with hypertension living in 43 LMICs and 40,795 people with diabetes living in 28 LMICs had their high blood pressure or diabetes treated well; i.e., they had these conditions diagnosed, treated, or controlled. We found that most proxy measures were not reflective of whether patients had their condition treated well. What do these findings mean? To judge countries’ progress towards ability to treat hypertension and diabetes requires directly assessing whether people with these diseases are getting the treatment that they need. The main limitation of the study was that a one-time measurement of blood pressure or blood glucose was used to define whether participants had high blood pressure or diabetes. To make a concrete clinical diagnosis requires more detailed investigation.

Background The prevalence of diabetes is increasing rapidly in low- and middle-income countries (LMICs), urgently requiring detailed evidence to guide the response of health systems to this epidemic. In an effort to understand at what step in the diabetes care continuum individuals are lost to care, and how this varies between countries and population groups, this study examined health system performance for diabetes among adults in 28 LMICs using a cascade of care approach. Methods and findings We pooled individual participant data from nationally representative surveys done between 2008 and 2016 in 28 LMICs. Diabetes was defined as fasting plasma glucose ≥ 7.0 mmol/l (126 mg/dl), random plasma glucose ≥ 11.1 mmol/l (200 mg/dl), HbA1c ≥ 6.5%, or reporting to be taking medication for diabetes. Stages of the care cascade were as follows: tested, diagnosed, lifestyle advice and/or medication given (“treated”), and controlled (HbA1c < 8.0% or equivalent). We stratified cascades of care by country, geographic region, World Bank income group, and individual-level characteristics (age, sex, educational attainment, household wealth quintile, and body mass index [BMI]). We then used logistic regression models with country-level fixed effects to evaluate predictors of (1) testing, (2) treatment, and (3) control. The final sample included 847,413 adults in 28 LMICs (8 low income, 9 lower-middle income, 11 upper-middle income). Survey sample size ranged from 824 in Guyana to 750,451 in India. The prevalence of diabetes was 8.8% (95% CI: 8.2%–9.5%), and the prevalence of undiagnosed diabetes was 4.8% (95% CI: 4.5%–5.2%). Health system performance for management of diabetes showed large losses to care at the stage of being tested, and low rates of diabetes control. Total unmet need for diabetes care (defined as the sum of those not tested, tested but undiagnosed, diagnosed but untreated, and treated but with diabetes not controlled) was 77.0% (95% CI: 74.9%–78.9%). Performance along the care cascade was significantly better in upper-middle income countries, but across all World Bank income groups, only half of participants with diabetes who were tested achieved diabetes control. Greater age, educational attainment, and BMI were associated with higher odds of being tested, being treated, and achieving control. The limitations of this study included the use of a single glucose measurement to assess diabetes, differences in the approach to wealth measurement across surveys, and variation in the date of the surveys. Conclusions The study uncovered poor management of diabetes along the care cascade, indicating large unmet need for diabetes care across 28 LMICs. Performance across the care cascade varied by World Bank income group and individual-level characteristics, particularly age, educational attainment, and BMI. This policy-relevant analysis can inform country-specific interventions and offers a baseline by which future progress can be measured., Using individual participant data from a large cohort study, Jennifer Manne-Goehler and colleagues reveal poor healthcare provision for diabetes patients in 28 LMICs., Author summary Why was this study done? Diabetes is a growing problem in low-income and middle-income countries, which already carry about 75% of the global diabetes burden, but there is limited evidence to guide policymaking in these settings. Prior studies have analyzed the cascade of care for diabetes in single-country or subnational contexts, but to our knowledge there have been no prior multi-country analyses on this subject. This study analyzes nationally representative data regarding health system performance for people who have diabetes, as defined by biological criteria, across a spectrum of levels of economic development. What did the researchers do and find? We harmonized nationally representative population-based survey data from 28 low-income and middle-income countries in 4 geographic regions, and used these data to construct the diabetes care cascade stratified by individual factors and national income. In this sample of over 800,000 adults, the overall prevalence of diabetes was 8.8% (95% CI: 8.2%–9.5%), of which 4.8% (95% CI: 4.5%–5.2%) was undiagnosed. In this study, we found that health system performance for diabetes showed large losses to care at the stage of detection (63.4% [95% CI: 56.7%–69.6%]) and low rates of diabetes control (22.8% [95% CI: 20.9%–24.9%]). Total unmet need for diabetes care (defined as the sum of undetected, detected but undiagnosed, diagnosed but untreated, and treated but not controlled diabetes) was 77.0% (95% CI: 74.9%–78.9%). Performance along the care cascade was better in upper-middle-income countries, but across all World Bank income groups, only 16%–25% of those with diabetes achieved diabetes control. Increasing age, educational attainment, and overweight or obesity were associated with higher odds of being tested, treated, and achieving control. What do these findings mean? There is a large unmet need for diabetes care that reflects the need for a greater investment of health resources to strengthen systems of care for this disease. Given that diabetes is considered a tracer condition for examining health systems, these findings also indicate that many countries face challenges in achieving universal health coverage.

Introduction: As diabetes increases in LMICs, there is a need for more evidence to guide health systems planning. We used a cascade of care approach to assess health system performance among adults (=15 years) in LMICs. Methods: We pooled individual data from 25 nationally representative surveys. Diabetes was defined as FBG=126 mg/dL, RBG=200 mg/dL, HbA1c=6.5%, or on medication. Stages of the care cascade included: screening, diagnosis, lifestyle advice and/or medication (“treatment”), and control (HbA1c =8.0%). Linear probability models were used to evaluate predictors (age, sex, education, and wealth quintile) of (1) treatment and (2) control. Results: Across all countries, the largest losses to care occur at screening (Table). While health service access increases with increasing country income group, rates of disease control are similar. Prediction models show that treatment is 31.1 percentage points (95% CI: 12.6-49.5) lower in the youngest age group compared to the oldest and that control is 3.9 percentage points (95% CI: 1.8-6.1) lower in men than women. Conclusions: Diabetes prevalence is substantial across LMICs, particularly in Latin America and the Caribbean. Overall rates of screening are low and only 23% of those with diabetes achieve control, which is less than half of that in the U.S. (64%). Policy interventions that link adults with diabetes to care and improve disease control in LMICs are needed. The diabetes cascade of care in 25 LMICS (N=92,141)RegionPrevalence (95% CI)Screened (95% CI)Diagnosed (95% CI)Completed (95% CI)Controlled (95% CI)Sub-Saharan Africa3.9 (3.0 - 5.0)53.1 (46.3 - 60.0)38.7 (32.9 - 45.0)36.0 (30.4 - 42.0)21.2 (16.7 - 26.3)Latin America & the Caribbean28.4 (23.7 - 33.7)79.8 (74.2 - 84.4)63.7 (57.9 - 69.3)48.9 (42.3 - 55.6)33.1 (26.8 - 40.1)South & Southeast Asia7.5 (7.1 - 7.9)57.5 (47.6 - 66.9)46.1 (36.9 - 55.6)45.2 (36.0 - 54.7)29.1 (22.0 - 37.5)Low-income group2.7 (2.1 - 3.5)39.9 (34.4 - 45.6)32.7 (27.2 - 38.5)31.4 (26.0 - 37.3)21.0 (16.0 - 27.1)Lower-middle income group7.7 (6.6 - 8.9)57.1 (46.0 - 67.6)38.2 (27.8 - 49.8)35.2 (25.0 - 47.0)23.9 (15.2 - 35.4)Upper-middle income group9.3 (5.5 - 15.3)68.9 (60.9 - 75.8)47.9 (40.3 - 55.5)41.7 (34.8 - 48.8)24.1 (19.1 - 30.0)Overall8.5 (5.9 - 12.0)57.9 (52.8 - 62.9)41.8 (37.1 - 46.6)37.6 (33.2 - 42.2)23.0 (19.5 - 27.0) Disclosure J. Manne-Goehler: None. P. Geldsetzer: None. D. Houinato: None. C. Houehanou: None. M.S. Gurung: None. G.M. Andall-Brereton: None. S. Crooks: None. K. Joseph: None. C. Wesseh: None. B. Norov: None. K.K. Aryal: None. K.B. Karki: None. P. Bovet: None. D. Labadarios: None. A. Stokes: None. M.T. Mayige: None. G.B. Kagaruki: None. D. Guwatudde: None. B.K. Silver: None. J.M. Jorgensen: None. S. Vollmer: None. J.I. Davies: None. T. Barnighausen: None. L.M. Jaacks: None.