Diabetes and pre-diabetes among persons aged 35 to 60 years in eastern Uganda: prevalence and associated factors
Our aim was to estimate the prevalence of abnormal glucose regulation (AGR) (i.e. diabetes and pre-diabetes) and its associated factors among people aged 35-60 years so as to clarify the relevance of targeted screening in rural Africa. A population-based survey of 1,497 people (786 women and 711 men...
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| Veröffentlicht in: | PloS one 2013-08, Vol.8 (8), p.e72554-e72554 |
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| creator | Mayega, Roy William Guwatudde, David Makumbi, Fredrick Nakwagala, Frederick Nelson Peterson, Stefan Tomson, Goran Ostenson, Claes-Goran |
| description | Our aim was to estimate the prevalence of abnormal glucose regulation (AGR) (i.e. diabetes and pre-diabetes) and its associated factors among people aged 35-60 years so as to clarify the relevance of targeted screening in rural Africa.
A population-based survey of 1,497 people (786 women and 711 men) aged 35-60 years was conducted in a predominantly rural Demographic Surveillance Site in eastern Uganda. Participants responded to a lifestyle questionnaire, following which their Body Mass Index (BMI) and Blood Pressure (BP) were measured. Fasting plasma glucose (FPG) was measured from capillary blood using On-Call® Plus (Acon) rapid glucose meters, following overnight fasting. AGR was defined as FPG ≥6.1 mmol L⁻¹ (World Health Organization (WHO) criteria or ≥5.6 mmol L⁻¹ (American Diabetes Association (ADA) criteria. Diabetes was defined as FPG >6.9 mmol L⁻¹, or being on diabetes treatment.
The mean age of participants was 45 years for men and 44 for women. Prevalence of diabetes was 7.4% (95%CI 6.1-8.8), while prevalence of pre-diabetes was 8.6% (95%CI 7.3-10.2) using WHO criteria and 20.2% (95%CI 17.5-22.9) with ADA criteria. Using WHO cut-offs, the prevalence of AGR was 2 times higher among obese persons compared with normal BMI persons (Adjusted Prevalence Rate Ratio (APRR) 1.9, 95%CI 1.3-2.8). Occupation as a mechanic, achieving the WHO recommended physical activity threshold, and higher dietary diversity were associated with lower likelihood of AGR (APRR 0.6, 95%CI 0.4-0.9; APRR 0.6, 95%CI 0.4-0.8; APRR 0.5, 95%CI 0.3-0.9 respectively). The direct medical cost of detecting one person with AGR was two US dollars with ADA and three point seven dollars with WHO cut-offs.
There is a high prevalence of AGR among people aged 35-60 years in this setting. Screening for high risk persons and targeted health education to address obesity, insufficient physical activity and non-diverse diets are necessary. |
| doi_str_mv | 10.1371/journal.pone.0072554 |
| format | Article |
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A population-based survey of 1,497 people (786 women and 711 men) aged 35-60 years was conducted in a predominantly rural Demographic Surveillance Site in eastern Uganda. Participants responded to a lifestyle questionnaire, following which their Body Mass Index (BMI) and Blood Pressure (BP) were measured. Fasting plasma glucose (FPG) was measured from capillary blood using On-Call® Plus (Acon) rapid glucose meters, following overnight fasting. AGR was defined as FPG ≥6.1 mmol L⁻¹ (World Health Organization (WHO) criteria or ≥5.6 mmol L⁻¹ (American Diabetes Association (ADA) criteria. Diabetes was defined as FPG >6.9 mmol L⁻¹, or being on diabetes treatment.
The mean age of participants was 45 years for men and 44 for women. Prevalence of diabetes was 7.4% (95%CI 6.1-8.8), while prevalence of pre-diabetes was 8.6% (95%CI 7.3-10.2) using WHO criteria and 20.2% (95%CI 17.5-22.9) with ADA criteria. Using WHO cut-offs, the prevalence of AGR was 2 times higher among obese persons compared with normal BMI persons (Adjusted Prevalence Rate Ratio (APRR) 1.9, 95%CI 1.3-2.8). Occupation as a mechanic, achieving the WHO recommended physical activity threshold, and higher dietary diversity were associated with lower likelihood of AGR (APRR 0.6, 95%CI 0.4-0.9; APRR 0.6, 95%CI 0.4-0.8; APRR 0.5, 95%CI 0.3-0.9 respectively). The direct medical cost of detecting one person with AGR was two US dollars with ADA and three point seven dollars with WHO cut-offs.
There is a high prevalence of AGR among people aged 35-60 years in this setting. Screening for high risk persons and targeted health education to address obesity, insufficient physical activity and non-diverse diets are necessary.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0072554</identifier><identifier>PMID: 23967317</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Adult ; Age ; Automation ; Behavior ; Blood ; Blood Glucose - metabolism ; Blood pressure ; Blood tests ; Body mass ; Body mass index ; Body size ; Capillary pressure ; Criteria ; Demographics ; Diabetes ; Diabetes mellitus ; Diabetes Mellitus, Type 2 - blood ; Diabetes Mellitus, Type 2 - diagnosis ; Diabetes Mellitus, Type 2 - epidemiology ; Diet ; Epidemiology ; Exercise ; Fasting ; Female ; Glucose ; Health Knowledge, Attitudes, Practice ; Health risks ; Health sciences ; Humans ; Hypertension ; Hypoglycemic agents ; Income - statistics & numerical data ; Low income groups ; Male ; Mass Screening - economics ; Maternal & child health ; Measuring instruments ; Medical screening ; Medicin och hälsovetenskap ; Middle Aged ; Mortality ; Multivariate analysis ; Nutrition research ; Obesity ; Physical activity ; Population ; Prediabetic state ; Prediabetic State - blood ; Prediabetic State - diagnosis ; Prediabetic State - epidemiology ; Prevalence ; Primary care ; Public health ; Risk ; Rural areas ; Rural Population - statistics & numerical data ; Type 2 diabetes ; Uganda - epidemiology</subject><ispartof>PloS one, 2013-08, Vol.8 (8), p.e72554-e72554</ispartof><rights>COPYRIGHT 2013 Public Library of Science</rights><rights>2013 Mayega et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License: https://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>2013 Mayega et al 2013 Mayega et al</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c817t-9c7875c3068faabfbcbbcfff7d85cc0ed5d9ce23c2a43b4b8123444be521f77a3</citedby><cites>FETCH-LOGICAL-c817t-9c7875c3068faabfbcbbcfff7d85cc0ed5d9ce23c2a43b4b8123444be521f77a3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3743823/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC3743823/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,552,727,780,784,864,885,2102,2928,23866,27924,27925,53791,53793</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/23967317$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink><backlink>$$Uhttps://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-207529$$DView record from Swedish Publication Index$$Hfree_for_read</backlink><backlink>$$Uhttp://kipublications.ki.se/Default.aspx?queryparsed=id:127258978$$DView record from Swedish Publication Index$$Hfree_for_read</backlink></links><search><contributor>Herder, Christian</contributor><creatorcontrib>Mayega, Roy William</creatorcontrib><creatorcontrib>Guwatudde, David</creatorcontrib><creatorcontrib>Makumbi, Fredrick</creatorcontrib><creatorcontrib>Nakwagala, Frederick Nelson</creatorcontrib><creatorcontrib>Peterson, Stefan</creatorcontrib><creatorcontrib>Tomson, Goran</creatorcontrib><creatorcontrib>Ostenson, Claes-Goran</creatorcontrib><title>Diabetes and pre-diabetes among persons aged 35 to 60 years in eastern Uganda: prevalence and associated factors</title><title>PloS one</title><addtitle>PLoS One</addtitle><description>Our aim was to estimate the prevalence of abnormal glucose regulation (AGR) (i.e. diabetes and pre-diabetes) and its associated factors among people aged 35-60 years so as to clarify the relevance of targeted screening in rural Africa.
A population-based survey of 1,497 people (786 women and 711 men) aged 35-60 years was conducted in a predominantly rural Demographic Surveillance Site in eastern Uganda. Participants responded to a lifestyle questionnaire, following which their Body Mass Index (BMI) and Blood Pressure (BP) were measured. Fasting plasma glucose (FPG) was measured from capillary blood using On-Call® Plus (Acon) rapid glucose meters, following overnight fasting. AGR was defined as FPG ≥6.1 mmol L⁻¹ (World Health Organization (WHO) criteria or ≥5.6 mmol L⁻¹ (American Diabetes Association (ADA) criteria. Diabetes was defined as FPG >6.9 mmol L⁻¹, or being on diabetes treatment.
The mean age of participants was 45 years for men and 44 for women. Prevalence of diabetes was 7.4% (95%CI 6.1-8.8), while prevalence of pre-diabetes was 8.6% (95%CI 7.3-10.2) using WHO criteria and 20.2% (95%CI 17.5-22.9) with ADA criteria. Using WHO cut-offs, the prevalence of AGR was 2 times higher among obese persons compared with normal BMI persons (Adjusted Prevalence Rate Ratio (APRR) 1.9, 95%CI 1.3-2.8). Occupation as a mechanic, achieving the WHO recommended physical activity threshold, and higher dietary diversity were associated with lower likelihood of AGR (APRR 0.6, 95%CI 0.4-0.9; APRR 0.6, 95%CI 0.4-0.8; APRR 0.5, 95%CI 0.3-0.9 respectively). The direct medical cost of detecting one person with AGR was two US dollars with ADA and three point seven dollars with WHO cut-offs.
There is a high prevalence of AGR among people aged 35-60 years in this setting. Screening for high risk persons and targeted health education to address obesity, insufficient physical activity and non-diverse diets are necessary.</description><subject>Adult</subject><subject>Age</subject><subject>Automation</subject><subject>Behavior</subject><subject>Blood</subject><subject>Blood Glucose - metabolism</subject><subject>Blood pressure</subject><subject>Blood tests</subject><subject>Body mass</subject><subject>Body mass index</subject><subject>Body size</subject><subject>Capillary pressure</subject><subject>Criteria</subject><subject>Demographics</subject><subject>Diabetes</subject><subject>Diabetes mellitus</subject><subject>Diabetes Mellitus, Type 2 - blood</subject><subject>Diabetes Mellitus, Type 2 - diagnosis</subject><subject>Diabetes Mellitus, Type 2 - epidemiology</subject><subject>Diet</subject><subject>Epidemiology</subject><subject>Exercise</subject><subject>Fasting</subject><subject>Female</subject><subject>Glucose</subject><subject>Health Knowledge, Attitudes, Practice</subject><subject>Health risks</subject><subject>Health sciences</subject><subject>Humans</subject><subject>Hypertension</subject><subject>Hypoglycemic agents</subject><subject>Income - statistics & numerical data</subject><subject>Low income groups</subject><subject>Male</subject><subject>Mass Screening - economics</subject><subject>Maternal & child health</subject><subject>Measuring instruments</subject><subject>Medical screening</subject><subject>Medicin och hälsovetenskap</subject><subject>Middle Aged</subject><subject>Mortality</subject><subject>Multivariate analysis</subject><subject>Nutrition research</subject><subject>Obesity</subject><subject>Physical activity</subject><subject>Population</subject><subject>Prediabetic state</subject><subject>Prediabetic State - blood</subject><subject>Prediabetic State - diagnosis</subject><subject>Prediabetic State - epidemiology</subject><subject>Prevalence</subject><subject>Primary care</subject><subject>Public health</subject><subject>Risk</subject><subject>Rural areas</subject><subject>Rural Population - statistics & numerical data</subject><subject>Type 2 diabetes</subject><subject>Uganda - epidemiology</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2013</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>D8T</sourceid><sourceid>DOA</sourceid><recordid>eNqNk-1r1TAUxosobk7_A9HCQBS817y1Sf0gjM2XwWCgbl9Dmp52mb1NTdLp_nvTe7vrKhtIPzQ5_T3PKU9ykuQ5RktMOX53aQfXqXbZ2w6WCHGSZexBsosLShY5QfThrfVO8sT7S4QyKvL8cbJDaJFzivlu0h8ZVUIAn6quSnsHi2pbWNmuSXtw3nZx10CV0iwNNs1Reg3K-dR0KSgfwHXpWRP16v3ocKVa6DSsDZX3VhsVorZWOljnnyaPatV6eDa995KzTx-_H35ZnJx-Pj48OFlogXlYFJoLnmmKclErVdalLktd1zWvRKY1giqrCg2EaqIYLVkpMKGMsRIygmvOFd1LXm58-9Z6OYXlJWYUsYIRnEXieENUVl3K3pmVctfSKiPXBesaqVwwugWZsSr2ZgWthGKiLgQuc1UIKkrCUMVF9FpsvPwv6Idy5jaVfsRVdCKcoSLyxb1872z1V3QjxCSesCjWvd7eqz0y5wfrPx8GSRDPyNjqwxTEUK6g0tAFp9p5x9mXzlzIxl5JyhkVhEaD15OBsz8H8EGujNfQtqoDO4yREs5ZRtgY6f4_6N3BT1QTb4o0XW1jXz2aygPGBRGCo7Ht8g4qPhWsjI6XvjaxPhO8mQkiE-B3aNTgvTz-9vX_2dPzOfvqFnsBqg0X3rZDMHEu5iDbgNpZ7x3U25AxkuPM3qQhx5mV08xG2YvbB7QV3Qwp_QNkIT3q</recordid><startdate>20130814</startdate><enddate>20130814</enddate><creator>Mayega, Roy William</creator><creator>Guwatudde, David</creator><creator>Makumbi, Fredrick</creator><creator>Nakwagala, Frederick Nelson</creator><creator>Peterson, Stefan</creator><creator>Tomson, Goran</creator><creator>Ostenson, Claes-Goran</creator><general>Public Library of Science</general><general>Public Library of Science (PLoS)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>IOV</scope><scope>ISR</scope><scope>3V.</scope><scope>7QG</scope><scope>7QL</scope><scope>7QO</scope><scope>7RV</scope><scope>7SN</scope><scope>7SS</scope><scope>7T5</scope><scope>7TG</scope><scope>7TM</scope><scope>7U9</scope><scope>7X2</scope><scope>7X7</scope><scope>7XB</scope><scope>88E</scope><scope>8AO</scope><scope>8C1</scope><scope>8FD</scope><scope>8FE</scope><scope>8FG</scope><scope>8FH</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABJCF</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BBNVY</scope><scope>BENPR</scope><scope>BGLVJ</scope><scope>BHPHI</scope><scope>C1K</scope><scope>CCPQU</scope><scope>D1I</scope><scope>DWQXO</scope><scope>FR3</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>GNUQQ</scope><scope>H94</scope><scope>HCIFZ</scope><scope>K9.</scope><scope>KB.</scope><scope>KB0</scope><scope>KL.</scope><scope>L6V</scope><scope>LK8</scope><scope>M0K</scope><scope>M0S</scope><scope>M1P</scope><scope>M7N</scope><scope>M7P</scope><scope>M7S</scope><scope>NAPCQ</scope><scope>P5Z</scope><scope>P62</scope><scope>P64</scope><scope>PATMY</scope><scope>PDBOC</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PTHSS</scope><scope>PYCSY</scope><scope>RC3</scope><scope>7X8</scope><scope>5PM</scope><scope>ACNBI</scope><scope>ADTPV</scope><scope>AOWAS</scope><scope>D8T</scope><scope>DF2</scope><scope>ZZAVC</scope><scope>DOA</scope></search><sort><creationdate>20130814</creationdate><title>Diabetes and pre-diabetes among persons aged 35 to 60 years in eastern Uganda: prevalence and associated factors</title><author>Mayega, Roy William ; Guwatudde, David ; Makumbi, Fredrick ; Nakwagala, Frederick Nelson ; Peterson, Stefan ; Tomson, Goran ; Ostenson, Claes-Goran</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c817t-9c7875c3068faabfbcbbcfff7d85cc0ed5d9ce23c2a43b4b8123444be521f77a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2013</creationdate><topic>Adult</topic><topic>Age</topic><topic>Automation</topic><topic>Behavior</topic><topic>Blood</topic><topic>Blood Glucose - 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blood</topic><topic>Prediabetic State - diagnosis</topic><topic>Prediabetic State - epidemiology</topic><topic>Prevalence</topic><topic>Primary care</topic><topic>Public health</topic><topic>Risk</topic><topic>Rural areas</topic><topic>Rural Population - statistics & numerical data</topic><topic>Type 2 diabetes</topic><topic>Uganda - epidemiology</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Mayega, Roy William</creatorcontrib><creatorcontrib>Guwatudde, David</creatorcontrib><creatorcontrib>Makumbi, Fredrick</creatorcontrib><creatorcontrib>Nakwagala, Frederick Nelson</creatorcontrib><creatorcontrib>Peterson, Stefan</creatorcontrib><creatorcontrib>Tomson, Goran</creatorcontrib><creatorcontrib>Ostenson, Claes-Goran</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: Science</collection><collection>ProQuest Central (Corporate)</collection><collection>Animal Behavior Abstracts</collection><collection>Bacteriology Abstracts (Microbiology B)</collection><collection>Biotechnology Research Abstracts</collection><collection>Nursing & Allied Health Database</collection><collection>Ecology Abstracts</collection><collection>Entomology Abstracts (Full archive)</collection><collection>Immunology Abstracts</collection><collection>Meteorological & Geoastrophysical Abstracts</collection><collection>Nucleic Acids Abstracts</collection><collection>Virology and AIDS Abstracts</collection><collection>Agricultural Science Collection</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Technology Research Database</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Natural Science Collection</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>Materials Science & Engineering Collection</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>Biological Science Collection</collection><collection>ProQuest Central</collection><collection>Technology Collection</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ProQuest One Community College</collection><collection>ProQuest Materials Science Collection</collection><collection>ProQuest Central Korea</collection><collection>Engineering Research Database</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Central Student</collection><collection>AIDS and Cancer Research Abstracts</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Materials Science Database</collection><collection>Nursing & Allied Health Database (Alumni Edition)</collection><collection>Meteorological & Geoastrophysical Abstracts - 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Academic</collection><collection>PubMed Central (Full Participant titles)</collection><collection>SWEPUB Uppsala universitet full text</collection><collection>SwePub</collection><collection>SwePub Articles</collection><collection>SWEPUB Freely available online</collection><collection>SWEPUB Uppsala universitet</collection><collection>SwePub Articles full text</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Mayega, Roy William</au><au>Guwatudde, David</au><au>Makumbi, Fredrick</au><au>Nakwagala, Frederick Nelson</au><au>Peterson, Stefan</au><au>Tomson, Goran</au><au>Ostenson, Claes-Goran</au><au>Herder, Christian</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Diabetes and pre-diabetes among persons aged 35 to 60 years in eastern Uganda: prevalence and associated factors</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2013-08-14</date><risdate>2013</risdate><volume>8</volume><issue>8</issue><spage>e72554</spage><epage>e72554</epage><pages>e72554-e72554</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>Our aim was to estimate the prevalence of abnormal glucose regulation (AGR) (i.e. diabetes and pre-diabetes) and its associated factors among people aged 35-60 years so as to clarify the relevance of targeted screening in rural Africa.
A population-based survey of 1,497 people (786 women and 711 men) aged 35-60 years was conducted in a predominantly rural Demographic Surveillance Site in eastern Uganda. Participants responded to a lifestyle questionnaire, following which their Body Mass Index (BMI) and Blood Pressure (BP) were measured. Fasting plasma glucose (FPG) was measured from capillary blood using On-Call® Plus (Acon) rapid glucose meters, following overnight fasting. AGR was defined as FPG ≥6.1 mmol L⁻¹ (World Health Organization (WHO) criteria or ≥5.6 mmol L⁻¹ (American Diabetes Association (ADA) criteria. Diabetes was defined as FPG >6.9 mmol L⁻¹, or being on diabetes treatment.
The mean age of participants was 45 years for men and 44 for women. Prevalence of diabetes was 7.4% (95%CI 6.1-8.8), while prevalence of pre-diabetes was 8.6% (95%CI 7.3-10.2) using WHO criteria and 20.2% (95%CI 17.5-22.9) with ADA criteria. Using WHO cut-offs, the prevalence of AGR was 2 times higher among obese persons compared with normal BMI persons (Adjusted Prevalence Rate Ratio (APRR) 1.9, 95%CI 1.3-2.8). Occupation as a mechanic, achieving the WHO recommended physical activity threshold, and higher dietary diversity were associated with lower likelihood of AGR (APRR 0.6, 95%CI 0.4-0.9; APRR 0.6, 95%CI 0.4-0.8; APRR 0.5, 95%CI 0.3-0.9 respectively). The direct medical cost of detecting one person with AGR was two US dollars with ADA and three point seven dollars with WHO cut-offs.
There is a high prevalence of AGR among people aged 35-60 years in this setting. Screening for high risk persons and targeted health education to address obesity, insufficient physical activity and non-diverse diets are necessary.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>23967317</pmid><doi>10.1371/journal.pone.0072554</doi><tpages>e72554</tpages><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2013-08, Vol.8 (8), p.e72554-e72554 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_1430494215 |
| source | MEDLINE; DOAJ Directory of Open Access Journals; SWEPUB Freely available online; Public Library of Science (PLoS) Journals Open Access; EZB-FREE-00999 freely available EZB journals; PubMed Central; Free Full-Text Journals in Chemistry |
| subjects | Adult Age Automation Behavior Blood Blood Glucose - metabolism Blood pressure Blood tests Body mass Body mass index Body size Capillary pressure Criteria Demographics Diabetes Diabetes mellitus Diabetes Mellitus, Type 2 - blood Diabetes Mellitus, Type 2 - diagnosis Diabetes Mellitus, Type 2 - epidemiology Diet Epidemiology Exercise Fasting Female Glucose Health Knowledge, Attitudes, Practice Health risks Health sciences Humans Hypertension Hypoglycemic agents Income - statistics & numerical data Low income groups Male Mass Screening - economics Maternal & child health Measuring instruments Medical screening Medicin och hälsovetenskap Middle Aged Mortality Multivariate analysis Nutrition research Obesity Physical activity Population Prediabetic state Prediabetic State - blood Prediabetic State - diagnosis Prediabetic State - epidemiology Prevalence Primary care Public health Risk Rural areas Rural Population - statistics & numerical data Type 2 diabetes Uganda - epidemiology |
| title | Diabetes and pre-diabetes among persons aged 35 to 60 years in eastern Uganda: prevalence and associated factors |
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