Health system performance for people with diabetes in 28 low- and middle-income countries: A cross-sectional study of nationally representative surveys
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 th...
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creator | Manne-Goehler, Jennifer Geldsetzer, Pascal Agoudavi, Kokou Andall-Brereton, Glennis Aryal, Krishna K Bicaba, Brice Wilfried Bovet, Pascal Brian, Garry Dorobantu, Maria Gathecha, Gladwell Singh Gurung, Mongal Guwatudde, David Msaidie, Mohamed Houehanou, Corine Houinato, Dismand Jorgensen, Jutta Mari Adelin Kagaruki, Gibson B Karki, Khem B Labadarios, Demetre Martins, Joao S Mayige, Mary T McClure, Roy Wong Mwalim, Omar Mwangi, Joseph Kibachio Norov, Bolormaa Quesnel-Crooks, Sarah Silver, Bahendeka K Sturua, Lela Tsabedze, Lindiwe Wesseh, Chea Stanford Stokes, Andrew Marcus, Maja Ebert, Cara Davies, Justine I Vollmer, Sebastian Atun, Rifat Bärnighausen, Till W Jaacks, Lindsay M |
description | 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.
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 |
doi_str_mv | 10.1371/journal.pmed.1002751 |
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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.
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.</description><identifier>ISSN: 1549-1676</identifier><identifier>ISSN: 1549-1277</identifier><identifier>EISSN: 1549-1676</identifier><identifier>DOI: 10.1371/journal.pmed.1002751</identifier><identifier>PMID: 30822339</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adolescent ; Adult ; Age ; Biology and Life Sciences ; Body mass ; Body mass index ; Cascade control ; Cross-Sectional Studies ; Delivery of Health Care - economics ; Delivery of Health Care - trends ; Developing countries ; Diabetes ; Diabetes mellitus ; Diabetes Mellitus - economics ; Diabetes Mellitus - epidemiology ; Diabetes Mellitus - therapy ; Diabetes therapy ; Diabetics ; Epidemics ; Epidemiology ; Evidence-based medicine ; Fasting ; Female ; Glucose ; Glycosylated hemoglobin ; Health care ; Health care industry ; Health sciences ; Health Services Needs and Demand - economics ; Health Services Needs and Demand - trends ; Health surveillance ; Health surveys ; Health Surveys - economics ; Health Surveys - trends ; Hospitals ; Humans ; Income - trends ; International aspects ; Laboratories ; Life Sciences ; Low income groups ; Male ; Medical care quality ; Medical research ; Medicine and Health Sciences ; Middle Aged ; Personal income ; Polls & surveys ; Population ; Population studies ; Poverty - economics ; Poverty - trends ; Preventive medicine ; Public health ; Regression analysis ; Research and Analysis Methods ; Santé publique et épidémiologie ; Social Sciences ; Surveys ; Systematic review ; Young Adult</subject><ispartof>PLoS medicine, 2019-03, Vol.16 (3), p.e1002751</ispartof><rights>COPYRIGHT 2019 Public Library of Science</rights><rights>2019 Manne-Goehler et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>Distributed under a Creative Commons Attribution 4.0 International License</rights><rights>2019 Manne-Goehler et al 2019 Manne-Goehler et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c779t-75db87f50324aa617726a85837fb516d49a3fcebbefbffb4d36659ab912734863</citedby><cites>FETCH-LOGICAL-c779t-75db87f50324aa617726a85837fb516d49a3fcebbefbffb4d36659ab912734863</cites><orcidid>0000-0002-1531-5983 ; 0000-0002-0242-4259 ; 0000-0001-8080-7872 ; 0000-0001-9295-0035 ; 0000-0002-1054-8104 ; 0000-0003-0622-6695 ; 0000-0002-7791-5167 ; 0000-0003-4904-2087 ; 0000-0001-8074-9393 ; 0000-0003-0801-0775 ; 0000-0002-8502-3636 ; 0000-0001-6481-6242 ; 0000-0002-8878-5505 ; 0000-0001-6834-1838 ; 0000-0002-4182-4212 ; 0000-0002-4232-1625 ; 0000-0003-4861-7870 ; 0000-0003-1940-5262</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6396901/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6396901/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,2096,2915,23845,27901,27902,53766,53768,79343,79344</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30822339$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://unilim.hal.science/hal-02060308$$DView record in HAL$$Hfree_for_read</backlink></links><search><contributor>Wareham, Nicholas J.</contributor><creatorcontrib>Manne-Goehler, Jennifer</creatorcontrib><creatorcontrib>Geldsetzer, Pascal</creatorcontrib><creatorcontrib>Agoudavi, Kokou</creatorcontrib><creatorcontrib>Andall-Brereton, Glennis</creatorcontrib><creatorcontrib>Aryal, Krishna K</creatorcontrib><creatorcontrib>Bicaba, Brice Wilfried</creatorcontrib><creatorcontrib>Bovet, Pascal</creatorcontrib><creatorcontrib>Brian, Garry</creatorcontrib><creatorcontrib>Dorobantu, Maria</creatorcontrib><creatorcontrib>Gathecha, Gladwell</creatorcontrib><creatorcontrib>Singh Gurung, Mongal</creatorcontrib><creatorcontrib>Guwatudde, David</creatorcontrib><creatorcontrib>Msaidie, Mohamed</creatorcontrib><creatorcontrib>Houehanou, Corine</creatorcontrib><creatorcontrib>Houinato, Dismand</creatorcontrib><creatorcontrib>Jorgensen, Jutta Mari Adelin</creatorcontrib><creatorcontrib>Kagaruki, Gibson B</creatorcontrib><creatorcontrib>Karki, Khem B</creatorcontrib><creatorcontrib>Labadarios, Demetre</creatorcontrib><creatorcontrib>Martins, Joao S</creatorcontrib><creatorcontrib>Mayige, Mary T</creatorcontrib><creatorcontrib>McClure, Roy Wong</creatorcontrib><creatorcontrib>Mwalim, Omar</creatorcontrib><creatorcontrib>Mwangi, Joseph Kibachio</creatorcontrib><creatorcontrib>Norov, Bolormaa</creatorcontrib><creatorcontrib>Quesnel-Crooks, Sarah</creatorcontrib><creatorcontrib>Silver, Bahendeka K</creatorcontrib><creatorcontrib>Sturua, Lela</creatorcontrib><creatorcontrib>Tsabedze, Lindiwe</creatorcontrib><creatorcontrib>Wesseh, Chea Stanford</creatorcontrib><creatorcontrib>Stokes, Andrew</creatorcontrib><creatorcontrib>Marcus, Maja</creatorcontrib><creatorcontrib>Ebert, Cara</creatorcontrib><creatorcontrib>Davies, Justine I</creatorcontrib><creatorcontrib>Vollmer, Sebastian</creatorcontrib><creatorcontrib>Atun, Rifat</creatorcontrib><creatorcontrib>Bärnighausen, Till W</creatorcontrib><creatorcontrib>Jaacks, Lindsay M</creatorcontrib><title>Health system performance for people with diabetes in 28 low- and middle-income countries: A cross-sectional study of nationally representative surveys</title><title>PLoS medicine</title><addtitle>PLoS Med</addtitle><description>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.
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.
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.</description><subject>Adolescent</subject><subject>Adult</subject><subject>Age</subject><subject>Biology and Life Sciences</subject><subject>Body mass</subject><subject>Body mass index</subject><subject>Cascade control</subject><subject>Cross-Sectional Studies</subject><subject>Delivery of Health Care - economics</subject><subject>Delivery of Health Care - trends</subject><subject>Developing countries</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes Mellitus - economics</subject><subject>Diabetes Mellitus - epidemiology</subject><subject>Diabetes Mellitus - therapy</subject><subject>Diabetes therapy</subject><subject>Diabetics</subject><subject>Epidemics</subject><subject>Epidemiology</subject><subject>Evidence-based medicine</subject><subject>Fasting</subject><subject>Female</subject><subject>Glucose</subject><subject>Glycosylated hemoglobin</subject><subject>Health care</subject><subject>Health care industry</subject><subject>Health sciences</subject><subject>Health Services Needs and Demand - economics</subject><subject>Health Services Needs and Demand - trends</subject><subject>Health surveillance</subject><subject>Health surveys</subject><subject>Health Surveys - economics</subject><subject>Health Surveys - trends</subject><subject>Hospitals</subject><subject>Humans</subject><subject>Income - trends</subject><subject>International aspects</subject><subject>Laboratories</subject><subject>Life Sciences</subject><subject>Low income groups</subject><subject>Male</subject><subject>Medical care quality</subject><subject>Medical research</subject><subject>Medicine and Health Sciences</subject><subject>Middle Aged</subject><subject>Personal income</subject><subject>Polls & surveys</subject><subject>Population</subject><subject>Population studies</subject><subject>Poverty - economics</subject><subject>Poverty - trends</subject><subject>Preventive medicine</subject><subject>Public health</subject><subject>Regression analysis</subject><subject>Research and Analysis Methods</subject><subject>Santé publique et épidémiologie</subject><subject>Social Sciences</subject><subject>Surveys</subject><subject>Systematic review</subject><subject>Young Adult</subject><issn>1549-1676</issn><issn>1549-1277</issn><issn>1549-1676</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqVk1tv0zAUxyMEYmPwDRBYQkLsIcWXxI55QKomYJMmJnF7tZzkuPXkxJ2ddPST8HVx125apz6A8hDn5Hf-5-aTZS8JnhAmyPtLP4Zeu8mig3ZCMKaiJI-yQ1IWMidc8Mf3zgfZsxgvEyOxxE-zA4YrShmTh9mfU9BumKO4igN0aAHB-NDpvgGUDunbLxyga5uQ1uoaBojI9ohWyPnrHOm-RZ1tWwe57RvfAWr82A_BQvyApqgJPsY8QjNYn3JFcRjbFfIG9XpjcSsUYBEgQj8k0xJQHMMSVvF59sRoF-HF9n2U_fz86cfJaX5-8eXsZHqeN0LIIRdlW1fClJjRQmtOhKBcV2XFhKlLwttCamYaqGswtTF10TLOS6lrSahgRcXZUfZ6o7twPqptT6OitKSUlzwJH2VnG6L1-lItgu10WCmvrbox-DBTOgy2caAIg1LWBauNwIUEkVIri4pyQ4VOMauk9XEbbazT2JpUddBuR3T3T2_nauaXijPJJSZJ4HgjMH_gdjo9V2sbppjjNN7lmn23DRb81QhxUJ2NDTine_BjqpGk9CgjjCb0zQN0fye21EynYm1vfMqxWYuqaVkRLCtcskTle6gZ9JAK8j0Ym8w7_GQPn54WOtvsdTjecUjMAL-HmR5jVGffv_0H-_Xf2Ytfu-zbe-z8Zoeid-P6VsddsNiAN6sQwNxNjWC13uPbTqv1HqvtHie3V_cvyp3T7eKyv_fIQjk</recordid><startdate>201903</startdate><enddate>201903</enddate><creator>Manne-Goehler, 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system performance for people with diabetes in 28 low- and middle-income countries: A cross-sectional study of nationally representative surveys</title><author>Manne-Goehler, Jennifer ; Geldsetzer, Pascal ; Agoudavi, Kokou ; Andall-Brereton, Glennis ; Aryal, Krishna K ; Bicaba, Brice Wilfried ; Bovet, Pascal ; Brian, Garry ; Dorobantu, Maria ; Gathecha, Gladwell ; Singh Gurung, Mongal ; Guwatudde, David ; Msaidie, Mohamed ; Houehanou, Corine ; Houinato, Dismand ; Jorgensen, Jutta Mari Adelin ; Kagaruki, Gibson B ; Karki, Khem B ; Labadarios, Demetre ; Martins, Joao S ; Mayige, Mary T ; McClure, Roy Wong ; Mwalim, Omar ; Mwangi, Joseph Kibachio ; Norov, Bolormaa ; Quesnel-Crooks, Sarah ; Silver, Bahendeka K ; Sturua, Lela ; Tsabedze, Lindiwe ; Wesseh, Chea Stanford ; Stokes, Andrew ; Marcus, Maja ; Ebert, Cara ; Davies, Justine I ; Vollmer, Sebastian ; Atun, Rifat ; Bärnighausen, Till W ; Jaacks, Lindsay M</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c779t-75db87f50324aa617726a85837fb516d49a3fcebbefbffb4d36659ab912734863</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>Adolescent</topic><topic>Adult</topic><topic>Age</topic><topic>Biology and Life Sciences</topic><topic>Body mass</topic><topic>Body mass index</topic><topic>Cascade control</topic><topic>Cross-Sectional Studies</topic><topic>Delivery of Health Care - economics</topic><topic>Delivery of Health Care - trends</topic><topic>Developing countries</topic><topic>Diabetes</topic><topic>Diabetes mellitus</topic><topic>Diabetes Mellitus - economics</topic><topic>Diabetes Mellitus - epidemiology</topic><topic>Diabetes Mellitus - therapy</topic><topic>Diabetes therapy</topic><topic>Diabetics</topic><topic>Epidemics</topic><topic>Epidemiology</topic><topic>Evidence-based medicine</topic><topic>Fasting</topic><topic>Female</topic><topic>Glucose</topic><topic>Glycosylated hemoglobin</topic><topic>Health care</topic><topic>Health care industry</topic><topic>Health sciences</topic><topic>Health Services Needs and Demand - economics</topic><topic>Health Services Needs and Demand - trends</topic><topic>Health surveillance</topic><topic>Health surveys</topic><topic>Health Surveys - economics</topic><topic>Health Surveys - trends</topic><topic>Hospitals</topic><topic>Humans</topic><topic>Income - trends</topic><topic>International aspects</topic><topic>Laboratories</topic><topic>Life Sciences</topic><topic>Low income groups</topic><topic>Male</topic><topic>Medical care quality</topic><topic>Medical research</topic><topic>Medicine and Health Sciences</topic><topic>Middle Aged</topic><topic>Personal income</topic><topic>Polls & surveys</topic><topic>Population</topic><topic>Population studies</topic><topic>Poverty - economics</topic><topic>Poverty - trends</topic><topic>Preventive medicine</topic><topic>Public health</topic><topic>Regression analysis</topic><topic>Research and Analysis Methods</topic><topic>Santé publique et épidémiologie</topic><topic>Social Sciences</topic><topic>Surveys</topic><topic>Systematic review</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Manne-Goehler, Jennifer</creatorcontrib><creatorcontrib>Geldsetzer, Pascal</creatorcontrib><creatorcontrib>Agoudavi, Kokou</creatorcontrib><creatorcontrib>Andall-Brereton, Glennis</creatorcontrib><creatorcontrib>Aryal, Krishna K</creatorcontrib><creatorcontrib>Bicaba, Brice Wilfried</creatorcontrib><creatorcontrib>Bovet, Pascal</creatorcontrib><creatorcontrib>Brian, Garry</creatorcontrib><creatorcontrib>Dorobantu, Maria</creatorcontrib><creatorcontrib>Gathecha, Gladwell</creatorcontrib><creatorcontrib>Singh Gurung, Mongal</creatorcontrib><creatorcontrib>Guwatudde, 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Maja</creatorcontrib><creatorcontrib>Ebert, Cara</creatorcontrib><creatorcontrib>Davies, Justine I</creatorcontrib><creatorcontrib>Vollmer, Sebastian</creatorcontrib><creatorcontrib>Atun, Rifat</creatorcontrib><creatorcontrib>Bärnighausen, Till W</creatorcontrib><creatorcontrib>Jaacks, Lindsay M</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Gale In Context: Opposing Viewpoints</collection><collection>Gale In Context: Canada</collection><collection>Gale In Context: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Neurosciences Abstracts</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Hospital 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UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>Hyper Article en Ligne (HAL)</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><collection>PLoS Medicine</collection><jtitle>PLoS medicine</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Manne-Goehler, Jennifer</au><au>Geldsetzer, Pascal</au><au>Agoudavi, Kokou</au><au>Andall-Brereton, Glennis</au><au>Aryal, Krishna K</au><au>Bicaba, Brice Wilfried</au><au>Bovet, Pascal</au><au>Brian, Garry</au><au>Dorobantu, Maria</au><au>Gathecha, Gladwell</au><au>Singh Gurung, Mongal</au><au>Guwatudde, David</au><au>Msaidie, Mohamed</au><au>Houehanou, Corine</au><au>Houinato, Dismand</au><au>Jorgensen, Jutta Mari Adelin</au><au>Kagaruki, Gibson B</au><au>Karki, Khem B</au><au>Labadarios, Demetre</au><au>Martins, Joao S</au><au>Mayige, Mary T</au><au>McClure, Roy Wong</au><au>Mwalim, Omar</au><au>Mwangi, Joseph Kibachio</au><au>Norov, Bolormaa</au><au>Quesnel-Crooks, Sarah</au><au>Silver, Bahendeka K</au><au>Sturua, Lela</au><au>Tsabedze, Lindiwe</au><au>Wesseh, Chea Stanford</au><au>Stokes, Andrew</au><au>Marcus, Maja</au><au>Ebert, Cara</au><au>Davies, Justine I</au><au>Vollmer, Sebastian</au><au>Atun, Rifat</au><au>Bärnighausen, Till W</au><au>Jaacks, Lindsay M</au><au>Wareham, Nicholas J.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Health system performance for people with diabetes in 28 low- and middle-income countries: A cross-sectional study of nationally representative surveys</atitle><jtitle>PLoS medicine</jtitle><addtitle>PLoS Med</addtitle><date>2019-03</date><risdate>2019</risdate><volume>16</volume><issue>3</issue><spage>e1002751</spage><pages>e1002751-</pages><issn>1549-1676</issn><issn>1549-1277</issn><eissn>1549-1676</eissn><abstract>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.
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.
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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>30822339</pmid><doi>10.1371/journal.pmed.1002751</doi><orcidid>https://orcid.org/0000-0002-1531-5983</orcidid><orcidid>https://orcid.org/0000-0002-0242-4259</orcidid><orcidid>https://orcid.org/0000-0001-8080-7872</orcidid><orcidid>https://orcid.org/0000-0001-9295-0035</orcidid><orcidid>https://orcid.org/0000-0002-1054-8104</orcidid><orcidid>https://orcid.org/0000-0003-0622-6695</orcidid><orcidid>https://orcid.org/0000-0002-7791-5167</orcidid><orcidid>https://orcid.org/0000-0003-4904-2087</orcidid><orcidid>https://orcid.org/0000-0001-8074-9393</orcidid><orcidid>https://orcid.org/0000-0003-0801-0775</orcidid><orcidid>https://orcid.org/0000-0002-8502-3636</orcidid><orcidid>https://orcid.org/0000-0001-6481-6242</orcidid><orcidid>https://orcid.org/0000-0002-8878-5505</orcidid><orcidid>https://orcid.org/0000-0001-6834-1838</orcidid><orcidid>https://orcid.org/0000-0002-4182-4212</orcidid><orcidid>https://orcid.org/0000-0002-4232-1625</orcidid><orcidid>https://orcid.org/0000-0003-4861-7870</orcidid><orcidid>https://orcid.org/0000-0003-1940-5262</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Adolescent Adult Age Biology and Life Sciences Body mass Body mass index Cascade control Cross-Sectional Studies Delivery of Health Care - economics Delivery of Health Care - trends Developing countries Diabetes Diabetes mellitus Diabetes Mellitus - economics Diabetes Mellitus - epidemiology Diabetes Mellitus - therapy Diabetes therapy Diabetics Epidemics Epidemiology Evidence-based medicine Fasting Female Glucose Glycosylated hemoglobin Health care Health care industry Health sciences Health Services Needs and Demand - economics Health Services Needs and Demand - trends Health surveillance Health surveys Health Surveys - economics Health Surveys - trends Hospitals Humans Income - trends International aspects Laboratories Life Sciences Low income groups Male Medical care quality Medical research Medicine and Health Sciences Middle Aged Personal income Polls & surveys Population Population studies Poverty - economics Poverty - trends Preventive medicine Public health Regression analysis Research and Analysis Methods Santé publique et épidémiologie Social Sciences Surveys Systematic review Young Adult |
title | Health system performance for people with diabetes in 28 low- and middle-income countries: A cross-sectional study of nationally representative surveys |
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