Evidence map›Paper›PMID 34562369›Full record

ArticleThe Lancet. Global health2021

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.

Sanjay Basu, David Flood, Pascal Geldsetzer, Michaela Theilmann, Maja E Marcus, Cara Ebert, Mary Mayige, Roy Wong-McClure, Farshad Farzadfar, Sahar Saeedi Moghaddam and 13 more

Erratum issuedOpen access · goldAbstract read
In one paragraph

Article in The Lancet. Global health, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 38 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
38citing papers in PubMed, 2 pooled it
10.6field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

38 citing papers in PubMed, 2 syntheses or guidelines pooled it, 48 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

23 authors at 17 institutions in 15 countries.

Sanjay BasuCenter for Primary Care, Harvard Medical School, Boston, MA, USA; Ariadne Labs, Harvard T H Chan School of Public Health, Brigham and Women's Hospital, Boston, MA, USA; School of Public Health, Imperial College, London, UK; Research and Population Health, Collective Health, San Francisco, CA, USA; Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON, Canada. Electronic address: sanjay_basu@hms.harvard.edu.
David FloodDivision of Hospital Medicine, Department of Internal Medicine, National Clinician Scholars Program, University of Michigan, Ann Arbor, MI, USA; Center for Indigenous Health Research, Wuqu' Kawoq, Tecpán, Guatemala; Research Center for the Prevention of Chronic Diseases, Institute of Nutrition of Central America and Panama, Guatemala City, Guatemala.
Pascal GeldsetzerDivision of Primary Care and Population Health, Department of Medicine, Stanford University, Stanford, CA, USA; Heidelberg Institute of Global Health, Heidelberg University and University Hospital, Heidelberg, Germany.
Michaela TheilmannHeidelberg Institute of Global Health, Heidelberg University and University Hospital, Heidelberg, Germany.
Maja E MarcusDepartment of Economics and Center for Modern Indian Studies, University of Goettingen, Goettingen, Germany.
Cara EbertRheinisch-Westfälisches Institut-Leibniz Institute for Economic Research, Essen, Germany.
Mary MayigeEpidemiology Department, National Institute for Medical Research, Dar es Salaam, Tanzania.
Roy Wong-McClureOffice of Epidemiology and Surveillance, Costa Rican Social Security Fund, San José, Costa Rica.
Farshad FarzadfarNon-Communicable Diseases Research Center, Endocrinology and Metabolism Population Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran; Tehran University of Medical Sciences, Tehran, Iran.
Sahar Saeedi MoghaddamEndocrinology and Metabolism Research Center, Endocrinology and Metabolism Clinical Sciences Institute, Tehran University of Medical Sciences, Tehran, Iran.
Kokou AgoudaviTogo Ministry of Health, Lome, Togo.
Bolormaa NorovNational Center for Public Health, Ulaanbaatar, Mongolia.
Corine HouehanouNational Training School for Senior Technicians in Public Health and Epidemiological Surveillance (ENATSE), University of Parakou, Parakou, Benin.
Glennis Andall-BreretonNon-Communicable Diseases, Caribbean Public Health Agency, Port of Spain, Trinidad and Tobago.
Mongal GurungHealth Research and Epidemiology Unit, Ministry of Health, Thimphu, Bhutan.
Garry BrianThe Fred Hollows Foundation, Sydney, NSW, Australia.
Pascal BovetMinistry of Health, Victoria, Seychelles.
Joao MartinsRector of the Univesidade Nacional Timor Lorosae, Dili, Timor-Leste.
Rifat AtunDepartment of Global Health and Population, Harvard T H Chan School of Public Health, Brigham and Women's Hospital, Boston, MA, USA.
Till BärnighausenDepartment of Global Health and Population, Harvard T H Chan School of Public Health, Brigham and Women's Hospital, Boston, MA, USA; Heidelberg Institute of Global Health, Heidelberg University and University Hospital, Heidelberg, Germany; Africa Health Research Institute, Somkhele, South Africa.
Sebastian VollmerHeidelberg Institute of Global Health, Heidelberg University and University Hospital, Heidelberg, Germany.
Jen Manne-GoehlerDivision of Infectious Diseases, Brigham and Women's Hospital, Boston, MA, USA; Medical Practice Evaluation Center, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
Justine DaviesInstitute for Applied Health Research, University of Birmingham, Birmingham, UK; Centre for Global Surgery, Department of Global Health, Stellenbosch University, Cape Town, South Africa; Medical Research Council-Wits University Rural Public Health and Health Transitions Research Unit, Faculty of Health Sciences, School of Public Health, University of the Witwatersrand, Johannesburg, South Africa.
Brigham and Women's Hospital · USTehran University of Medical Sciences · IRUniversity Hospital Heidelberg · DECaribbean Public Health Agency · TTCosta Rican Department of Social Security · CRImperial College London · GBMinistry of Health · SCNational Center for Communicable Diseases · MNNational Institute for Medical Research · TZRWI – Leibniz Institute for Economic Research · DEStanford University · USTogolese Ministry of Health · TGUniversidade da Paz · TLUniversité de Parakou · BJUniversity of Birmingham · GBUniversity of Göttingen · DEUniversity of Michigan–Ann Arbor · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundGiven 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.

methodsWe constructed a microsimulation model to estimate disability-adjusted life-years (DALYs) lost and health-care 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 m

findingsWe 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 cost-effectiveness ratio of $1362 per DALY averted (IQR 1304-1409).

interpretationReducing 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.

fundingNone.

Indexed as

AdultAgedAged, 80 and overCardiovascular DiseasesCross-Sectional StudiesDeveloping CountriesDiabetes ComplicationsDiabetes MellitusFemaleGlobal HealthHumansMaleMiddle AgedModels, TheoreticalRisk Factors

Identifiers

PMID34562369
PMCPMC8526364
OpenAlexW3200174887

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.