Browsing by Author "Garry Brian"

Now showing 1 - 3 of 3
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    Data Resource Profile: The Global Health and Population Project on Access to Care for Cardiometabolic Diseases (HPACC)
    (2022-12-13) Jennifer Manne-Goehler; Michaela Theilmann; David Flood; Maja E Marcus; Glennis Andall-Brereton; Kokou Agoudavi; William Andres Lopez Arboleda; Krishna K Aryal; Brice Bicaba; Pascal Bovet; Luisa Campos Caldeira Brant; Garry Brian; Grace Chamberlin; Geoffrey Chen; Albertino Damasceno; Maria Dorobantu; Matthew Dunn; Cara Ebert; Farshad Farzadfar; Mongal Singh Gurung; David Guwatudde; Corine Houehanou; Dismand Houinato; Nahla Hwalla; Jutta M Adelin Jorgensen; Khem B Karki; Demetre Labadarios; Nuno Lunet; Deborah Carvalho Malta; Joao S Martins; Mary T Mayige; Roy Wong McClure; Sahar Saeedi Moghaddam; Kibachio J Mwangi; Omar Mwalim; Bolormaa Norov; Sarah Quesnel-Crooks; Sabrina Rhode; Jacqueline A Seiglie; Abla Sibai; Bahendeka K Silver; Lela Sturua; Andrew Stokes; Adil Supiyev; Lindiwe Tsabedze; Zhaxybay Zhumadilov; Lindsay M Jaacks; Rifat Atun; Justine I Davies
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    Estimated effect of increased diagnosis, treatment, and control of diabetes and its associated cardiovascular risk factors among low-income and middle-income countries: a microsimulation model
    (2021-09-22) Sanjay Basu; David Flood; Pascal Geldsetzer; Michaela Theilmann; Maja E Marcus; Cara Ebert; Mary Mayige; Roy Wong-McClure; Farshad Farzadfar; Sahar Saeedi Moghaddam; Kokou Agoudavi; Bolormaa Norov; Corine Houehanou; Glennis Andall-Brereton; Mongal Gurung; Garry Brian; Pascal Bovet; Joao Martins; Rifat Atun; Till Bärnighausen; Sebastian Vollmer; Jen Manne-Goehler; Justine Davies
    Background Given the increasing prevalence of diabetes in low-income and middle-income countries (LMICs), we aimed to estimate the health and cost implications of achieving different targets for diagnosis, treatment, and control of diabetes and its associated cardiovascular risk factors among LMICs. Methods We constructed a microsimulation model to estimate disability-adjusted life-years (DALYs) lost and healthcare costs of diagnosis, treatment, and control of blood pressure, dyslipidaemia, and glycaemia among people with diabetes in LMICs. We used individual participant data—specifically from the subset of people who were defined as having any type of diabetes by WHO standards—from nationally representative, cross-sectional surveys (2006–18) spanning 15 world regions to estimate the baseline 10-year risk of atherosclerotic cardiovascular disease (defined as fatal and non-fatal myocardial infarction and stroke), heart failure (ejection fraction of <40%, with New York Heart Association class III or IV functional limitations), end-stage renal disease (defined as an estimated glomerular filtration rate <15 mL/min per 1∙73 m² or needing dialysis or transplant), retinopathy with severe vision loss (<20/200 visual acuity as measured by the Snellen chart), and neuropathy with pressure sensation loss (assessed by the Semmes-Weinstein 5∙07/10 g monofilament exam). We then used data from meta-analyses of randomised controlled trials to estimate the reduction in risk and the WHO OneHealth tool to estimate costs in reaching either 60% or 80% of diagnosis, treatment initiation, and control targets for blood pressure, dyslipidaemia, and glycaemia recommended by WHO guidelines. Costs were updated to 2020 International Dollars, and both costs and DALYs were computed over a 10-year policy planning time horizon at a 3% annual discount rate. Findings We obtained data from 23 678 people with diabetes from 67 countries. The median estimated 10-year risk was 10∙0% (IQR 4∙0–18∙0) for cardiovascular events, 7∙8% (5∙1–11∙8) for neuropathy with pressure sensation loss, 7∙2% (5∙6–9∙4) for end-stage renal disease, 6∙0% (4∙2–8∙6) for retinopathy with severe vision loss, and 2∙6% (1∙2–5∙3) for congestive heart failure. A target of 80% diagnosis, 80% treatment, and 80% control would be expected to reduce DALYs lost from diabetes complications from a median population-weighted loss to 1097 DALYs per 1000 population over 10 years (IQR 1051–1155), relative to a baseline of 1161 DALYs, primarily from reduced cardiovascular events (down from a median of 143 to 117 DALYs per 1000 population) due to blood pressure and statin treatment, with comparatively little effect from glycaemic control. The target of 80% diagnosis, 80% treatment, and 80% control would be expected to produce an overall incremental cost-effectiveness ratio of US$1362 per DALY averted (IQR 1304–1409), with the majority of decreased costs from reduced cardiovascular event management, counterbalanced by increased costs for blood pressure and statin treatment, producing an overall incremental costeffectiveness ratio of $1362 per DALY averted (IQR 1304–1409). Interpretation Reducing complications from diabetes in LMICs is likely to require a focus on scaling up blood pressure and statin medication treatment initiation and blood pressure medication titration rather than focusing on increasing screening to increase diabetes diagnosis, or a glycaemic treatment and control among people with diabetes.
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    Rural-Urban Differences in Diabetes Care and Control in 42 Low- and Middle-Income Countries: A Cross-sectional Study of Nationally Representative Individual-Level Data
    (2022-09) David Flood; Pascal Geldsetzer; Kokou Agoudavi; Krishna K. Aryal; Luisa Campos Caldeira Brant; Garry Brian; Maria Dorobantu; Farshad Farzadfar; Oana Gheorghe-Fronea; Mongal Singh Gurung; David Guwatudde; Corine Houehanou; Jutta M. Adelin Jorgensen; Dimple Kondal; Demetre Labadarios; Maja E. Marcus; Mary Mayige; Mana Moghimi; Bolormaa Norov; Gaston Perman; Sarah Quesnel-Crooks; Mohammad-Mahdi Rashidi; Sahar Saeedi Moghaddam; Jacqueline A. Seiglie; Silver K. Bahendeka; Eric Steinbrook; Michaela Theilmann; Lisa J. Ware; Sebastian Vollmer; Rifat Atun; Justine I. Davies; Mohammed K. Ali; Peter Rohloff; Jennifer Manne-Goehler
    OBJECTIVE Diabetes prevalence is increasing rapidly in rural areas of low- and middle-income countries (LMICs), but there are limited data on the performance of health systems in delivering equitable and effective care to rural populations. We therefore assessed rural-urban differences in diabetes care and control in LMICs. RESEARCH DESIGN AND METHODS We pooled individual-level data from nationally representative health surveys in 42 countries. We used Poisson regression models to estimate age-adjusted differences in the proportion of individuals with diabetes in rural versus urban areas achieving performance measures for the diagnosis, treatment, and control of diabetes and associated cardiovascular risk factors. We examined differences across the pooled sample, by sex, and by country. RESULTS The pooled sample from 42 countries included 840,110 individuals (35,404 with diabetes). Compared with urban populations with diabetes, rural populations had 15–30% lower relative risk of achieving performance measures for diabetes diagnosis and treatment. Rural populations with diagnosed diabetes had a 14% (95% CI 5–22%) lower relative risk of glycemic control, 6% (95% CI 25 to 16%) lower relative risk of blood pressure control, and 23% (95% CI 2–39%) lower relative risk of cholesterol control. Rural women with diabetes had lower achievement of performance measures relating to control than urban women, whereas among men, differences were small. CONCLUSIONS Rural populations with diabetes experience substantial inequities in the achievement of diabetes performance measures in LMICs. Programs and policies aiming to strengthen global diabetes care must consider the unique challenges experienced by rural populations.