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Inequalities in the prevalence of diabetes mellitus and its risk factors in Sri Lanka: a lower middle income country

BACKGROUND: Explorations into quantifying the inequalities for diabetes mellitus (DM) and its risk factors are scarce in low and lower middle income countries (LICs/LMICs). The aims of this study were to assess the inequalities of DM and its risk factors in a suburban district of Sri Lanka. METHODS:...

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Autores principales: De Silva, Ambepitiyawaduge Pubudu, De Silva, Sudirikku Hennadige Padmal, Haniffa, Rashan, Liyanage, Isurujith Kongala, Jayasinghe, Saroj, Katulanda, Prasad, Wijeratne, Chandrika Neelakanthi, Wijeratne, Sumedha, Rajapaksa, Lalini Chandika
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5905173/
https://www.ncbi.nlm.nih.gov/pubmed/29665834
http://dx.doi.org/10.1186/s12939-018-0759-3
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author De Silva, Ambepitiyawaduge Pubudu
De Silva, Sudirikku Hennadige Padmal
Haniffa, Rashan
Liyanage, Isurujith Kongala
Jayasinghe, Saroj
Katulanda, Prasad
Wijeratne, Chandrika Neelakanthi
Wijeratne, Sumedha
Rajapaksa, Lalini Chandika
author_facet De Silva, Ambepitiyawaduge Pubudu
De Silva, Sudirikku Hennadige Padmal
Haniffa, Rashan
Liyanage, Isurujith Kongala
Jayasinghe, Saroj
Katulanda, Prasad
Wijeratne, Chandrika Neelakanthi
Wijeratne, Sumedha
Rajapaksa, Lalini Chandika
author_sort De Silva, Ambepitiyawaduge Pubudu
collection PubMed
description BACKGROUND: Explorations into quantifying the inequalities for diabetes mellitus (DM) and its risk factors are scarce in low and lower middle income countries (LICs/LMICs). The aims of this study were to assess the inequalities of DM and its risk factors in a suburban district of Sri Lanka. METHODS: A sample of 1300 participants, (aged 35–64 years) randomly selected using a stratified multi-stage cluster sampling method, were studied employing a cross sectional descriptive design. The socioeconomic indicators (SEIs) of the individual were education level and occupational category, and at the household level, the household income, social status level and area deprivation level. DM was diagnosed if the fasting plasma glucose was ≥126 and a body mass index (BMI) of > 27.5 kg/m(2) was considered high. Asian cut-off values were used for high waist circumference (WC). Validated tools were used to assess the diet and level of physical activity. The slope index of inequality (SII), relative index of inequality (RII) and concentration index (CI) were used to assess inequalities. RESULTS: The prevalence of DM and its risk factors (at individual or household level) showed no consistent relationship with the three measures of inequality (SII, RII and CI) of the different indices of socio economic status (education, occupation, household income, social status index or area unsatisfactory basic needs index). The prevalence of diabetes showed a more consistent pro-rich distribution in females compared to males. Of the risk factors in males and females, the most consistent and significant pro-rich relationship was for high BMI and WC. In males, the significant positive relationship with high BMI for SII ranged from 0.18 to 0.35, and RII from 1.56 to 2.25. For high WC, the values were: SII from 0.13 to 0.27 and RII from 1.9 to 3.97. In females the significant positive relationship with high BMI in SII ranged from 0.13 to 0.29, and RII from 2.3 to 4.98. For high WC the values were: SII from 028 to 0.4 and RII 1.99 to 2.39. Of the other risk factors, inadequate fruit intake showed a consistent significant pro-poor distribution only in males using SII (− 0.25 to − 0.36) and in both sexes using CI. Smoking also showed a pro-poor distribution in males especially using individual measures of socio-economic status (i.e. education and occupation). CONCLUSIONS: The results show a variable relationship between socioeconomic status and prevalence of diabetes and its risk factors. The inequalities in the prevalence of diabetes and risk factors vary depending on gender and the measures used. The study suggests that measures to prevent diabetes should focus on targeting specific factors based on sex and socioeconomic status. The priority target areas for interventions should include prevention of obesity (BMI and central obesity) specifically in more affluent females. Males who have a low level of education and in non-skilled occupations should be especially targeted to reduce smoking and increase fruit intake.
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spelling pubmed-59051732018-04-24 Inequalities in the prevalence of diabetes mellitus and its risk factors in Sri Lanka: a lower middle income country De Silva, Ambepitiyawaduge Pubudu De Silva, Sudirikku Hennadige Padmal Haniffa, Rashan Liyanage, Isurujith Kongala Jayasinghe, Saroj Katulanda, Prasad Wijeratne, Chandrika Neelakanthi Wijeratne, Sumedha Rajapaksa, Lalini Chandika Int J Equity Health Research BACKGROUND: Explorations into quantifying the inequalities for diabetes mellitus (DM) and its risk factors are scarce in low and lower middle income countries (LICs/LMICs). The aims of this study were to assess the inequalities of DM and its risk factors in a suburban district of Sri Lanka. METHODS: A sample of 1300 participants, (aged 35–64 years) randomly selected using a stratified multi-stage cluster sampling method, were studied employing a cross sectional descriptive design. The socioeconomic indicators (SEIs) of the individual were education level and occupational category, and at the household level, the household income, social status level and area deprivation level. DM was diagnosed if the fasting plasma glucose was ≥126 and a body mass index (BMI) of > 27.5 kg/m(2) was considered high. Asian cut-off values were used for high waist circumference (WC). Validated tools were used to assess the diet and level of physical activity. The slope index of inequality (SII), relative index of inequality (RII) and concentration index (CI) were used to assess inequalities. RESULTS: The prevalence of DM and its risk factors (at individual or household level) showed no consistent relationship with the three measures of inequality (SII, RII and CI) of the different indices of socio economic status (education, occupation, household income, social status index or area unsatisfactory basic needs index). The prevalence of diabetes showed a more consistent pro-rich distribution in females compared to males. Of the risk factors in males and females, the most consistent and significant pro-rich relationship was for high BMI and WC. In males, the significant positive relationship with high BMI for SII ranged from 0.18 to 0.35, and RII from 1.56 to 2.25. For high WC, the values were: SII from 0.13 to 0.27 and RII from 1.9 to 3.97. In females the significant positive relationship with high BMI in SII ranged from 0.13 to 0.29, and RII from 2.3 to 4.98. For high WC the values were: SII from 028 to 0.4 and RII 1.99 to 2.39. Of the other risk factors, inadequate fruit intake showed a consistent significant pro-poor distribution only in males using SII (− 0.25 to − 0.36) and in both sexes using CI. Smoking also showed a pro-poor distribution in males especially using individual measures of socio-economic status (i.e. education and occupation). CONCLUSIONS: The results show a variable relationship between socioeconomic status and prevalence of diabetes and its risk factors. The inequalities in the prevalence of diabetes and risk factors vary depending on gender and the measures used. The study suggests that measures to prevent diabetes should focus on targeting specific factors based on sex and socioeconomic status. The priority target areas for interventions should include prevention of obesity (BMI and central obesity) specifically in more affluent females. Males who have a low level of education and in non-skilled occupations should be especially targeted to reduce smoking and increase fruit intake. BioMed Central 2018-04-17 /pmc/articles/PMC5905173/ /pubmed/29665834 http://dx.doi.org/10.1186/s12939-018-0759-3 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
De Silva, Ambepitiyawaduge Pubudu
De Silva, Sudirikku Hennadige Padmal
Haniffa, Rashan
Liyanage, Isurujith Kongala
Jayasinghe, Saroj
Katulanda, Prasad
Wijeratne, Chandrika Neelakanthi
Wijeratne, Sumedha
Rajapaksa, Lalini Chandika
Inequalities in the prevalence of diabetes mellitus and its risk factors in Sri Lanka: a lower middle income country
title Inequalities in the prevalence of diabetes mellitus and its risk factors in Sri Lanka: a lower middle income country
title_full Inequalities in the prevalence of diabetes mellitus and its risk factors in Sri Lanka: a lower middle income country
title_fullStr Inequalities in the prevalence of diabetes mellitus and its risk factors in Sri Lanka: a lower middle income country
title_full_unstemmed Inequalities in the prevalence of diabetes mellitus and its risk factors in Sri Lanka: a lower middle income country
title_short Inequalities in the prevalence of diabetes mellitus and its risk factors in Sri Lanka: a lower middle income country
title_sort inequalities in the prevalence of diabetes mellitus and its risk factors in sri lanka: a lower middle income country
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5905173/
https://www.ncbi.nlm.nih.gov/pubmed/29665834
http://dx.doi.org/10.1186/s12939-018-0759-3
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