Vollmer, Sebastian

[["dc.bibliographiccitation.artnumber","e1002751"],["dc.bibliographiccitation.issue","3"],["dc.bibliographiccitation.journal","PLOS Medicine"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Manne-Goehler, Jennifer"],["dc.contributor.author","Geldsetzer, Pascal"],["dc.contributor.author","Agoudavi, Kokou"],["dc.contributor.author","Andall-Brereton, Glennis"],["dc.contributor.author","Aryal, Krishna K."],["dc.contributor.author","Bicaba, Brice Wilfried"],["dc.contributor.author","Bovet, Pascal"],["dc.contributor.author","Brian, Garry"],["dc.contributor.author","Dorobantu, Maria"],["dc.contributor.author","Gathecha, Gladwell"],["dc.contributor.author","Singh Gurung, Mongal"],["dc.contributor.author","Guwatudde, David"],["dc.contributor.author","Msaidie, Mohamed"],["dc.contributor.author","Houehanou, Corine"],["dc.contributor.author","Houinato, Dismand"],["dc.contributor.author","Jorgensen, Jutta Mari Adelin"],["dc.contributor.author","Kagaruki, Gibson B."],["dc.contributor.author","Karki, Khem B."],["dc.contributor.author","Labadarios, Demetre"],["dc.contributor.author","Martins, Joao S."],["dc.contributor.author","Mayige, Mary T."],["dc.contributor.author","McClure, Roy Wong"],["dc.contributor.author","Mwalim, Omar"],["dc.contributor.author","Mwangi, Joseph Kibachio"],["dc.contributor.author","Norov, Bolormaa"],["dc.contributor.author","Quesnel-Crooks, Sarah"],["dc.contributor.author","Silver, Bahendeka K."],["dc.contributor.author","Sturua, Lela"],["dc.contributor.author","Tsabedze, Lindiwe"],["dc.contributor.author","Wesseh, Chea Stanford"],["dc.contributor.author","Stokes, Andrew"],["dc.contributor.author","Marcus, Maja"],["dc.contributor.author","Ebert, Cara"],["dc.contributor.author","Davies, Justine I."],["dc.contributor.author","Vollmer, Sebastian"],["dc.contributor.author","Atun, Rifat"],["dc.contributor.author","Bärnighausen, Till W."],["dc.contributor.author","Jaacks, Lindsay M."],["dc.date.accessioned","2019-07-09T11:50:19Z"],["dc.date.available","2019-07-09T11:50:19Z"],["dc.date.issued","2019"],["dc.description.abstract","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."],["dc.identifier.doi","10.1371/journal.pmed.1002751"],["dc.identifier.pmid","30822339"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15910"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59747"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1549-1676"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","300"],["dc.subject.ddc","320"],["dc.title","Health system performance for people with diabetes in 28 low- and middle-income countries: A cross-sectional study of nationally representative surveys"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","e1002581"],["dc.bibliographiccitation.issue","6"],["dc.bibliographiccitation.journal","PLoS Medicine"],["dc.bibliographiccitation.volume","15"],["dc.contributor.author","Geldsetzer, Pascal"],["dc.contributor.author","Manne-Goehler, Jennifer"],["dc.contributor.author","Theilmann, Michaela"],["dc.contributor.author","Davies, Justine I."],["dc.contributor.author","Awasthi, Ashish"],["dc.contributor.author","Danaei, Goodarz"],["dc.contributor.author","Gaziano, Thomas A."],["dc.contributor.author","Vollmer, Sebastian"],["dc.contributor.author","Jaacks, Lindsay M."],["dc.contributor.author","Bärnighausen, Till"],["dc.contributor.author","Atun, Rifat"],["dc.contributor.editor","Peiris, David"],["dc.date.accessioned","2020-12-10T18:42:04Z"],["dc.date.available","2020-12-10T18:42:04Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.1371/journal.pmed.1002581"],["dc.identifier.eissn","1549-1676"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15669"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77794"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.relation.orgunit","Wirtschaftswissenschaftliche Fakultät"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Geographic and sociodemographic variation of cardiovascular disease risk in India: A cross-sectional study of 797,540 adults"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Nutrition Journal"],["dc.bibliographiccitation.volume","19"],["dc.contributor.author","McKenzie, Briar L."],["dc.contributor.author","Santos, Joseph Alvin"],["dc.contributor.author","Geldsetzer, Pascal"],["dc.contributor.author","Davies, Justine"],["dc.contributor.author","Manne-Goehler, Jennifer"],["dc.contributor.author","Gurung, Mongal Singh"],["dc.contributor.author","Sturua, Lela"],["dc.contributor.author","Gathecha, Gladwell"],["dc.contributor.author","Aryal, Krishna K."],["dc.contributor.author","Tsabedze, Lindiwe"],["dc.contributor.author","Andall-Brereton, Glennis"],["dc.contributor.author","Bärnighausen, Till"],["dc.contributor.author","Atun, Rifat"],["dc.contributor.author","Vollmer, Sebastian"],["dc.contributor.author","Woodward, Mark"],["dc.contributor.author","Jaacks, Lindsay M."],["dc.contributor.author","Webster, Jacqui"],["dc.date.accessioned","2020-12-10T18:38:59Z"],["dc.date.available","2020-12-10T18:38:59Z"],["dc.date.issued","2020"],["dc.identifier.doi","10.1186/s12937-019-0517-4"],["dc.identifier.eissn","1475-2891"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/17123"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77500"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Evaluation of sex differences in dietary behaviours and their relationship with cardiovascular risk factors: a cross-sectional study of nationally representative surveys in seven low- and middle-income countries"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e1003841"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","PLoS Medicine"],["dc.bibliographiccitation.volume","18"],["dc.contributor.author","Marcus, Maja E."],["dc.contributor.author","Ebert, Cara"],["dc.contributor.author","Geldsetzer, Pascal"],["dc.contributor.author","Theilmann, Michaela"],["dc.contributor.author","Bicaba, Brice Wilfried"],["dc.contributor.author","Andall-Brereton, Glennis"],["dc.contributor.author","Bovet, Pascal"],["dc.contributor.author","Farzadfar, Farshad"],["dc.contributor.author","Singh Gurung, Mongal"],["dc.contributor.author","Houehanou, Corine"],["dc.contributor.author","Malekpour, Mohammad-Reza"],["dc.contributor.author","Moghaddam, Sahar Saeedi"],["dc.contributor.author","Mohammadi, Esmaeil"],["dc.contributor.author","Quesnel-Crooks, Sarah"],["dc.contributor.author","Davies, Justine I."],["dc.contributor.author","Hlatky, Mark A."],["dc.contributor.author","Bärnighausen, Till W."],["dc.contributor.author","Atun, Rifat"],["dc.contributor.author","Jaacks, Lindsay M."],["dc.contributor.author","Manne-Goehler, Jennifer"],["dc.contributor.author","Vollmer, Sebastian"],["dc.date.accessioned","2021-12-01T09:23:07Z"],["dc.date.available","2021-12-01T09:23:07Z"],["dc.date.issued","2021"],["dc.description.abstract","Background As the prevalence of hypercholesterolemia is increasing in low- and middle-income countries (LMICs), detailed evidence is urgently needed to guide the response of health systems to this epidemic. This study sought to quantify unmet need for hypercholesterolemia care among adults in 35 LMICs. Methods and findings We pooled individual-level data from 129,040 respondents aged 15 years and older from 35 nationally representative surveys conducted between 2009 and 2018. Hypercholesterolemia care was quantified using cascade of care analyses in the pooled sample and by region, country income group, and country. Hypercholesterolemia was defined as (i) total cholesterol (TC) ≥240 mg/dL or self-reported lipid-lowering medication use and, alternatively, as (ii) low-density lipoprotein cholesterol (LDL-C) ≥160 mg/dL or self-reported lipid-lowering medication use. Stages of the care cascade for hypercholesterolemia were defined as follows: screened (prior to the survey), aware of diagnosis, treated (lifestyle advice and/or medication), and controlled (TC <200 mg/dL or LDL-C <130 mg/dL). We further estimated how age, sex, education, body mass index (BMI), current smoking, having diabetes, and having hypertension are associated with cascade progression using modified Poisson regression models with survey fixed effects. High TC prevalence was 7.1% (95% CI: 6.8% to 7.4%), and high LDL-C prevalence was 7.5% (95% CI: 7.1% to 7.9%). The cascade analysis showed that 43% (95% CI: 40% to 45%) of study participants with high TC and 47% (95% CI: 44% to 50%) with high LDL-C ever had their cholesterol measured prior to the survey. About 31% (95% CI: 29% to 33%) and 36% (95% CI: 33% to 38%) were aware of their diagnosis; 29% (95% CI: 28% to 31%) and 33% (95% CI: 31% to 36%) were treated; 7% (95% CI: 6% to 9%) and 19% (95% CI: 18% to 21%) were controlled. We found substantial heterogeneity in cascade performance across countries and higher performances in upper-middle-income countries and the Eastern Mediterranean, Europe, and Americas. Lipid screening was significantly associated with older age, female sex, higher education, higher BMI, comorbid diagnosis of diabetes, and comorbid diagnosis of hypertension. Awareness of diagnosis was significantly associated with older age, higher BMI, comorbid diagnosis of diabetes, and comorbid diagnosis of hypertension. Lastly, treatment of hypercholesterolemia was significantly associated with comorbid hypertension and diabetes, and control of lipid measures with comorbid diabetes. The main limitations of this study are a potential recall bias in self-reported information on received health services as well as diminished comparability due to varying survey years and varying lipid guideline application across country and clinical settings. Conclusions Cascade performance was poor across all stages, indicating large unmet need for hypercholesterolemia care in this sample of LMICs—calling for greater policy and research attention toward this cardiovascular disease (CVD) risk factor and highlighting opportunities for improved prevention of CVD."],["dc.description.sponsorship","Open-Access-Publikationsfonds 2021"],["dc.identifier.doi","10.1371/journal.pmed.1003841"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/94563"],["dc.language.iso","en"],["dc.notes.intern","DOI-Import GROB-478"],["dc.relation.eissn","1549-1676"],["dc.rights","CC BY 4.0"],["dc.title","Unmet need for hypercholesterolemia care in 35 low- and middle-income countries: A cross-sectional study of nationally representative surveys"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e1002801"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","PLOS Medicine"],["dc.bibliographiccitation.volume","16"],["dc.contributor.author","Prenissl, Jonas"],["dc.contributor.author","Manne-Goehler, Jennifer"],["dc.contributor.author","Jaacks, Lindsay M."],["dc.contributor.author","Prabhakaran, Dorairaj"],["dc.contributor.author","Awasthi, Ashish"],["dc.contributor.author","Bischops, Anne Christine"],["dc.contributor.author","Atun, Rifat"],["dc.contributor.author","Bärnighausen, Till"],["dc.contributor.author","Davies, Justine I."],["dc.contributor.author","Vollmer, Sebastian"],["dc.contributor.author","Geldsetzer, Pascal"],["dc.date.accessioned","2019-07-09T11:51:16Z"],["dc.date.available","2019-07-09T11:51:16Z"],["dc.date.issued","2019"],["dc.description.abstract","BACKGROUND: Evidence on where in the hypertension care process individuals are lost to care, and how this varies among states and population groups in a country as large as India, is essential for the design of targeted interventions and to monitor progress. Yet, to our knowledge, there has not yet been a nationally representative analysis of the proportion of adults who reach each step of the hypertension care process in India. This study aimed to determine (i) the proportion of adults with hypertension who have been screened, are aware of their diagnosis, take antihypertensive treatment, and have achieved control and (ii) the variation of these care indicators among states and sociodemographic groups. METHODS AND FINDINGS: We used data from a nationally representative household survey carried out from 20 January 2015 to 4 December 2016 among individuals aged 15-49 years in all states and union territories (hereafter \"states\") of the country. The stages of the care process-computed among those with hypertension at the time of the survey-were (i) having ever had one's blood pressure (BP) measured before the survey (\"screened\"), (ii) having been diagnosed (\"aware\"), (iii) currently taking BP-lowering medication (\"treated\"), and (iv) reporting being treated and not having a raised BP (\"controlled\"). We disaggregated these stages by state, rural-urban residence, sex, age group, body mass index, tobacco consumption, household wealth quintile, education, and marital status. In total, 731,864 participants were included in the analysis. Hypertension prevalence was 18.1% (95% CI 17.8%-18.4%). Among those with hypertension, 76.1% (95% CI 75.3%-76.8%) had ever received a BP measurement, 44.7% (95% CI 43.6%-45.8%) were aware of their diagnosis, 13.3% (95% CI 12.9%-13.8%) were treated, and 7.9% (95% CI 7.6%-8.3%) had achieved control. Male sex, rural location, lower household wealth, and not being married were associated with greater losses at each step of the care process. Between states, control among individuals with hypertension varied from 2.4% (95% CI 1.7%-3.3%) in Nagaland to 21.0% (95% CI 9.8%-39.6%) in Daman and Diu. At 38.0% (95% CI 36.3%-39.0%), 28.8% (95% CI 28.5%-29.2%), 28.4% (95% CI 27.7%-29.0%), and 28.4% (95% CI 27.8%-29.0%), respectively, Puducherry, Tamil Nadu, Sikkim, and Haryana had the highest proportion of all adults (irrespective of hypertension status) in the sampled age range who had hypertension but did not achieve control. The main limitation of this study is that its results cannot be generalized to adults aged 50 years and older-the population group in which hypertension is most common. CONCLUSIONS: Hypertension prevalence in India is high, but the proportion of adults with hypertension who are aware of their diagnosis, are treated, and achieve control is low. Even after adjusting for states' economic development, there is large variation among states in health system performance in the management of hypertension. Improvements in access to hypertension diagnosis and treatment are especially important among men, in rural areas, and in populations with lower household wealth."],["dc.identifier.doi","10.1371/journal.pmed.1002801"],["dc.identifier.pmid","31050680"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16092"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59914"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","300"],["dc.subject.ddc","320"],["dc.title","Hypertension screening, awareness, treatment, and control in India: A nationally representative cross-sectional study among individuals aged 15 to 49 years"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","92"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","BMC Medicine"],["dc.bibliographiccitation.volume","17"],["dc.contributor.author","Prenissl, Jonas"],["dc.contributor.author","Jaacks, Lindsay M"],["dc.contributor.author","Mohan, Viswanathan"],["dc.contributor.author","Manne-Goehler, Jennifer"],["dc.contributor.author","Davies, Justine I"],["dc.contributor.author","Awasthi, Ashish"],["dc.contributor.author","Bischops, Anne C"],["dc.contributor.author","Atun, Rifat"],["dc.contributor.author","Bärnighausen, Till"],["dc.contributor.author","Vollmer, Sebastian"],["dc.contributor.author","Geldsetzer, Pascal"],["dc.date.accessioned","2019-07-09T11:51:23Z"],["dc.date.available","2019-07-09T11:51:23Z"],["dc.date.issued","2019"],["dc.description.abstract","Abstract Background Understanding where adults with diabetes in India are lost in the diabetes care cascade is essential for the design of targeted health interventions and to monitor progress in health system performance for managing diabetes over time. This study aimed to determine (i) the proportion of adults with diabetes in India who have reached each step of the care cascade and (ii) the variation of these cascade indicators among states and socio-demographic groups. Methods We used data from a population-based household survey carried out in 2015 and 2016 among women and men aged 15–49 years in all states of India. Diabetes was defined as a random blood glucose (RBG) ≥ 200 mg/dL or reporting to have diabetes. The care cascade—constructed among those with diabetes—consisted of the proportion who (i) reported having diabetes (“aware”), (ii) had sought treatment (“treated”), and (iii) had sought treatment and had a RBG < 200 mg/dL (“controlled”). The care cascade was disaggregated by state, rural-urban location, age, sex, household wealth quintile, education, and marital status. Results This analysis included 729,829 participants. Among those with diabetes (19,453 participants), 52.5% (95% CI, 50.6–54.4%) were “aware”, 40.5% (95% CI, 38.6–42.3%) “treated”, and 24.8% (95% CI, 23.1–26.4%) “controlled”. Living in a rural area, male sex, less household wealth, and lower education were associated with worse care cascade indicators. Adults with untreated diabetes constituted the highest percentage of the adult population (irrespective of diabetes status) aged 15 to 49 years in Goa (4.2%; 95% CI, 3.2–5.2%) and Tamil Nadu (3.8%; 95% CI, 3.4–4.1%). The highest absolute number of adults with untreated diabetes lived in Tamil Nadu (1,670,035; 95% CI, 1,519,130–1,812,278) and Uttar Pradesh (1,506,638; 95% CI, 1,419,466–1,589,832). Conclusions There are large losses to diabetes care at each step of the care cascade in India, with the greatest loss occurring at the awareness stage. While health system performance for managing diabetes varies greatly among India’s states, improvements are particularly needed for rural areas, those with less household wealth and education, and men. Although such improvements will likely have the greatest benefits for population health in Goa and Tamil Nadu, large states with a low diabetes prevalence but a high absolute number of adults with untreated diabetes, such as Uttar Pradesh, should not be neglected."],["dc.identifier.doi","10.1186/s12916-019-1325-6"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16116"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59938"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.publisher","BioMed Central"],["dc.relation.orgunit","Wirtschaftswissenschaftliche Fakultät"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0/"],["dc.title","Variation in health system performance for managing diabetes among states in India: a cross-sectional study of individuals aged 15 to 49 years"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]