research on diabetes in mauritius

Diabetes Management System in Mauritius: Current Perspectives and Potentials of Pervasive Healthcare Technologies

First Online: 16 November 2021

Cite this chapter

Geshwaree Huzooree 7 ,
Kavi Khedo 8 &
Noorjehan Joonas 9

Part of the book series: EAI/Springer Innovations in Communication and Computing ((EAISICC))

333 Accesses

Diabetes is a growing scourge in Mauritius, and the complications associated with it have major implications for public health in terms of high social and economic burden and low quality of life (QoL). The aim of the study was to measure the satisfaction level of diabetics with the diabetes management system in Mauritius. The study also assessed the success factors affecting the QoL of diabetics and their willingness to adopt pervasive healthcare systems (PHSs) for diabetes monitoring. A cross-sectional survey was carried out, and three well-validated scales, including the Mauritius version of the Audit of Diabetes-Dependent Quality of Life (ADDQoL), the Diabetes Treatment Satisfaction Questionnaire (DTSQ), and Technology Acceptance questionnaire, were used. Based on the statistical analysis and findings, future research directions are defined to identify the critical factors for the adoption of PHSs, which could significantly help to improve the diabetes management system in Mauritius.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Available as PDF
Read on any device
Instant download
Own it forever
Available as EPUB and PDF
Compact, lightweight edition
Dispatched in 3 to 5 business days
Free shipping worldwide - see info
Durable hardcover edition

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Ng, J. K. Y., Wang, J., Lam, K. Y., Ho, C., Kam, C. C. H. C., & Han, S. (2014). Capturing and analyzing pervasive data for Smarthealth. In Proceedings of the 28th international conference on advanced information networking and applications (AINA) (pp. 985–992). IEEE.

Google Scholar

Huzooree, G., Khedo, K. K., & Joonas, N. (2017). Pervasive mobile healthcare systems for chronic disease monitoring. Health Informatics Journal, 25 (2), 267–291.

Article Google Scholar

Huzooree, G., Khedo, K. K., & Joonas, N. (2019). Data reliability and quality in body area networks for diabetes monitoring. In R. Maheswar, G. Kanagachidambaresan, & R. Jayaparvathy (Eds.), Body area network challenges and solutions (pp. 55–86). EAI/Springer Innovations in Communication and Computing.

Chapter Google Scholar

Huzooree, G., Khedo, K. K., & Joonas, N. (2016). Wireless body area network system architecture for real-time diabetes monitoring. In Emerging trends in electrical, electronic and communications engineering. ELECOM 2016 (pp. 262–271). Springer.

Huzooree, G., Khedo, K. K., & Joonas, N. (2018). Low-cost body area network for monitoring of diabetic patient. In Design and development of affordable healthcare technologies (pp. 135–164). IGI Global.

Gayan, M.. (2015). Mauritius NCD survey 2015 report.

IDF. (2018). IDF SEA members . Retrieved October 5, 2019, from https://www.idf.org/our-network/regions-members/south-east-asia/members/96-mauritius.html

WHO. (2017). Biennium report 2016|2017 . Retrieved March 20, 2019, from https://www.afro.who.int/sites/default/files/2018-03/WHOMauritius Biennium Report 2016-2017.pdf

MOHQL. (2007). The national service framework for diabetes (NSFD) . Retrieved March 20, 2019, from https://extranet.who.int/ncdccs/Data/MUS_B6_NSFD.pdf

International Diabetes Federation. (2010). A guide to national diabetes programmes . Retrieved March 20, 2019, from https://www.idf.org/component/attachments/attachments.html?id=1576&task=download

Bradley, C. (1994). The diabetes treatment satisfaction questionnaire: DTSQ. In Handbook of psychology and diabetes: A guide to psychological measurement in diabetes research and practice (Vol. 111, p. 132). Routledge.

Bradley, C., Todd, C., Gorton, T., Symonds, E., Martin, A., & Plowright, R. (1999). The development of an individualized questionnaire measure of perceived impact of diabetes on quality of life: The ADDQoL. Quality of Life Research, 8 (1), 79–91.

Pinnock, H., Slack, R., Pagliari, C., Price, D., & Sheikh, A. (2006). Professional and patient attitudes to using mobile phone technology to monitor asthma: Questionnaire survey. Primary Care Respiratory Journal, 15 (4), 237–245.

Costa, F. A., Guerreiro, J. P., & Duggan, C. (2006). An audit of diabetes dependent quality of life (ADDQoL) for Portugal: Exploring validity and reliability. Pharmacy Practice (Granada), 4 (3), 123–128.

Holmanová, E., & Žiaková, K. (2009). Audit diabetes-dependent quality of life questionnaire: Usefulness in diabetes self-management education in the Slovak population. Journal of Clinical Nursing, 18 (9), 1276–1286.

McGillicuddy, W. J., et al. (2013). Patient attitudes toward mobile phone-based health monitoring: Questionnaire study among kidney transplant recipients. Journal of Medical Internet Research, 15 (1), e6.

Kuznetsov, L. (2014). Diabetes-specific quality of life but not health status is independently associated with glycaemic control among patients with type 2 diabetes: A cross-sectional analysis of the ADDITION-Europe trial cohort. Diabetes Research and Clinical Practice, 104 (2), 281–287.

Chung, J. O., Cho, D. H., Chung, D. J., & Chung, M. Y. (2013). Assessment of factors associated with the quality of life in Korean type 2 diabetic patients. Internal Medicine, 52 (2), 179–185.

Papazafiropoulou, A. K. (2015). Diabetes - dependent quality of life (ADDQOL) and affecting factors in patients with diabetes mellitus type 2 in Greece. BMC Research Notes , 4–9.

Turk, E., Rupel, V. P., Tapajner, A., Leyshon, S., & Isola, A. (2013). An audit of diabetes-dependent quality of life (ADDQOL) in older patients with diabetes mellitus type 2 in Slovenia. Value in Health Regional Issues, 2 (2), 248–253.

Wang, H. F., & Yeh, M. C. (2013). The quality of life of adults with type 2 diabetes in a hospital care clinic in Taiwan. Quality of Life Research, 22 (3), 577–584.

Testa, M. A., Simonson, D. C., & Turner, R. R. (1998). Valuing quality of life and improvements in glycemic control in people with type 2 diabetes. Diabetes Care, 21 (3), 44–52.

Weinberger, M. (1995). A nurse-coordinated intervention for primary care patients with non-insulin-dependent diabetes mellitus. Journal of General Internal Medicine, 10 (2), 9–66.

Johnson, J. A., Nowatzki, T. E., & Coons, S. J. (1996). Health-related quality of life of diabetic Pima Indians. Medical Care, 34 (2), 97–102.

Quinn, C. C., Clough, S. S., Minor, J. M., Lender, D., Okafor, M. C., & Gruber-Baldini, A. (2008). WellDoc™ mobile diabetes management randomized controlled trial: Change in clinical and behavioral outcomes and patient and physician satisfaction. Diabetes Technology & Therapeutics, 10 (3), 160–168.

Bostock, Y., Hanley, J., McGown, D., Pinnock, H., Padfield, P., & McKinstry, B. (2009). The acceptability to patients and professionals of remote blood pressure monitoring using mobile phones. Primary Health Care Research & Development, 10 (4), 299–308.

Seto, E., Leonard, K. J., Masino, C., Cafazzo, J. A., Barnsley, J., & Ross, H. J. (2010). Attitudes of heart failure patients and health care providers towards mobile phone-based remote monitoring. Journal of Medical Internet Research, 12 (4), e55.

Sidi, F., Panahy, P. H. S., Affendey, L. S., Jabar, M. A., Ibrahim, H., & Mustapha, A. (2012). Data quality: A survey of data quality dimensions. In Proceedings of the international conference on information retrieval & knowledge management (CAMP) (pp. 300–304). IEEE.

Pike, M., Mustafa, N. M., Towey, D., & Brusic, V. (2019). Sensor networks and data management in healthcare: Emerging technologies and new challenges. In International computer software and applications conference (Vol. 1, pp. 834–839). IEEE.

Li, Y., Sperrin, M., Martin, G. P., Ashcroft, D. M., & Staa, T. P. (2020). Examining the impact of data quality and completeness of electronic health records on predictions of patients’ risks of cardiovascular disease. International Journal of Medical Informatics, 133 , 104046.

Sun, Y., Lo, F. P., & Lo, B. (2019). Security and privacy for the internet of medical things enabled healthcare systems: A survey. IEEE Access, 7 , 183339–183355.

M. Uzair, M. A. Khan, S. Mann, and M. Imran, “A review: Ubiquitous healthcare monitoring with mobile phone,” 2018 9th IEEE annu. ubiquitous comput. electron. mob. commun. conf. UEMCON 2018, IEEE pp. 988–996, 2018.

Sindhu, C., & Hegde, N. P. (2017). A novel integrated framework to ensure better data quality in big data analytics over cloud environment. International Journal of Electrical and Computer Engineering, 7 (5), 2798–2805.

Haghi, M. (2020). A flexible and pervasive IoT-based healthcare platform for physiological and environmental parameters monitoring. IEEE Internet of Things, 7 (6), 5628–5647.

Jesus, G., Casimiro, A., & Oliveira, A. (2017). A survey on data quality for dependable monitoring in wireless sensor networks. Sensors, 17 (9), 2010.

Wu, F., Wu, T., & Yuce, M. R. (2019). Design and implementation of a wearable sensor network system for IoT-connected safety and health applications. In IEEE 5th world forum on internet of things (pp. 87–90). IEEE.

Chen, M., & Fowler, M. L. (2003). The importance of data compression for energy efficiency in sensor networks. In Proceedings of the conference on information sciences and systems (p. 13). IEEE.

Alemdar, H., & Ersoy, C. (2010). Wireless sensor networks for healthcare: A survey. Computer Networks, 54 (15), 2688–2710.

Lai, X., Liu, Q., Wei, X., Wang, W., Zhou, G., & Han, G. (2013). A survey of body sensor networks. Sensors, 13 (5), 5406–5447.

Dhagarra, D., Goswami, M., & Kumar, G. (2020). Impact of trust and privacy concerns on technology acceptance in healthcare: An Indian perspective. International Journal of Medical Informatics, 141 , 104164.

Pramanik, I., Lau, R. Y. K., Kalam, A., Hossain, S., Hossain, K., & Karmaker, B. K. (2020). Healthcare informatics and analytics in big data. Expert Systems with Applications, 152 , 113388.

Triantafyllidis, A., Koutkias, V. G., Chouvarda, I., & Maglaveras, N. (2014). Development and usability of a personalized sensor - based system for pervasive healthcare. In Proceedings of the 36th annual international conference of the IEEE engineering in medicine and biology society (pp. 6623–6626). IEEE.

Jeong, I. C., Bychkov, D., & Searson, P. C. (2019). Wearable devices for precision medicine and health state monitoring. IEEE Transactions on Biomedical Engineering, 66 (5), 1242–1258.

Rodbard, D. (2016). Continuous glucose monitoring: A review of successes, challenges, and opportunities. Diabetes Technology & Therapeutics, 18 (2), 2–13.

Alaiad, A., & Zhou, L. (2017). Patients’ adoption of WSN-based smart home healthcare systems: An integrated model of facilitators and barriers. IEEE Transactions on Professional Communication, 60 (1), 4–23.

Kumar, M. (2014). Security issues and privacy concerns in the implementation of wireless body area network. In Proceedings of the international conference on information technology (ICIT) (pp. 58–62). IEEE.

Touati, F., & Tabish, R. (2013). U-healthcare system: State-of-the-art review and challenges. Journal of Medical Systems, 37 (3), 9949.

Karkouch, A., Mousannif, H., Al Moatassime, H., & Thomas, N. (2018). A model-driven framework for data quality management in the Internet of Things. Journal of Ambient Intelligence and Humanized Computing, 9 (4), 977–998.

Mashoufi, M., Ayatollahi, H., & Khorasani-zavareh, D. (2018). A review of data quality assessment in emergency medical services. The Open Medical Informatics Journal, 12 , 19–32.

Bai, L., Meredith, R., & Burstein, F. (2018). A data quality framework, method and tools for managing data quality in a health care setting: An action case study. Journal of Decision Systems, 27 (1), 144–154.

Download references

Author information

Authors and affiliations.

Charles Telfair Campus Curtin University, Moka, Mauritius

Geshwaree Huzooree

University of Mauritius, Moka, Mauritius

Ministry of Health and Quality of Life Victoria Hospital Candos, Quatre Bornes, Mauritius

Noorjehan Joonas

You can also search for this author in PubMed Google Scholar

Editor information

Editors and affiliations.

Information Technology Department, College of Applied Sciences, University of Technology and Applied Sciences, Ibri, Oman

Mohammad Shahid Husain

Universiti Pendidikan Sultan Idris, Tanjung Malim, Malaysia

Muhamad Hariz Bin Muhamad Adnan

Computer Science and Engineering Department, Integral University, Lucknow, India

Mohammad Zunnun Khan

Data Science Institute, National University of Ireland, Galway, Ireland

Saurabh Shukla

Global Health IT Product Consultant, Rhapsody, Auckland, New Zealand

Fahad U Khan

Rights and permissions

Reprints and permissions

Copyright information

About this chapter

Huzooree, G., Khedo, K., Joonas, N. (2022). Diabetes Management System in Mauritius: Current Perspectives and Potentials of Pervasive Healthcare Technologies. In: Husain, M.S., Adnan, M.H.B.M., Khan, M.Z., Shukla, S., Khan, F.U. (eds) Pervasive Healthcare. EAI/Springer Innovations in Communication and Computing. Springer, Cham. https://doi.org/10.1007/978-3-030-77746-3_20

Download citation

DOI : https://doi.org/10.1007/978-3-030-77746-3_20

Published : 16 November 2021

Publisher Name : Springer, Cham

Print ISBN : 978-3-030-77745-6

Online ISBN : 978-3-030-77746-3

eBook Packages : Engineering Engineering (R0)

Share this chapter

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Publish with us

Policies and ethics

Find a journal
Track your research

Explaining the increase of diabetes prevalence and plasma glucose in Mauritius

Affiliation.

1 Baker IDI Heart and Diabetes Institute, Melbourne, Australia. [email protected]
PMID: 22100964
PMCID: PMC3241310
DOI: 10.2337/dc11-0886

Objective: Secular trends in the epidemiology of diabetes are best described by studying the same population over time, but few such studies exist. Using surveys from Mauritius in 1987 and 2009, we examined 1) the change in the prevalence of diabetes, 2) the extent to which changes in traditional diabetes risk factors explained the increase, and 3) the change in the distribution of plasma glucose levels over time.

Research design and methods: Independent population-based surveys were undertaken in Mauritius in 1987 and 2009 using similar methodology in adults aged 20-74 years. Physical measurements and fasting blood samples were taken, and an oral glucose tolerance test was performed at both surveys.

Results: The age-standardized prevalence of diabetes in 2009 was 22.3% (95% CI 20.0-24.6) among men and 20.2% (18.3-22.3) among women, representing an increase since 1987 of 64 and 62% among men and women, respectively. Concurrent changes in the distribution of age, ethnicity, waist circumference, BMI, physical activity, smoking, family history of diabetes, and hypertension explained more of the increase in the prevalence of diabetes in men than in women. Increases in plasma glucose (especially fasting glucose) were seen across the population but were greater at the upper levels.

Conclusions: In Mauritius, there has been a marked increase in diabetes prevalence over 22 years. This mainly results from changes in traditional risk factors, leading to population-wide increases in plasma glucose levels. Interventions to control this escalation of diabetes should focus on population-wide strategies.

Publication types

Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Blood Glucose / metabolism*
Body Mass Index
Diabetes Mellitus / epidemiology*
Glucose Intolerance / epidemiology
Glucose Tolerance Test
Mauritius / epidemiology
Middle Aged
Risk Factors
Blood Glucose

Grants and funding

DK-25446/DK/NIDDK NIH HHS/United States

GIS – 03 April 2023: The Minister of Health and Wellness, Dr Kailesh Kumar Singh Jagutpal, underlined the need for health practitioners, all stakeholders concerned by the sector as well as different authorities at all levels of the Government, to interpret the findings of the Mauritius Non-Communicable Diseases (NCD) Survey 2021 and take the necessary preventive measures.

The Minister was speaking, this morning, during the official launching of a dissemination workshop on the findings of the Mauritius NCD Survey 2021 at Hennessy Park Hotel, in Ebène.

He highlighted that the findings demonstrate crucial information about the prevalence of NCDs in the country, namely different diseases pattern and, most importantly, the disease burden that will be felt in the coming years.

Dr Kailesh Jagutpal also urged clinicians to take these findings into consideration and to make the necessary changes in their day-to-day practice so that they are able to, in turn, make a change in the lifestyle of their patients. This response will help curb the curve of the NCDs prevalence, thus contribute in building a healthier Mauritius with a brighter future, he remarked.

As for the World Health Organization (WHO) Representative in Mauritius, Dr Anne Marie Ancia, she rejoiced that Mauritius is keen to make much effort to increase the healthy life expectancy of its population. The NCD Survey she said, will enable the Government to elaborate important strategies and actions to tackle NCD morbidity and mortality.

The WHO Representative recalled that the WHO set forth in 2012, a target of reducing the prevalence of major NCDs by 25 % by 2025, which she emphasised, can be achieved in Mauritius following all the Governmental initiatives being undertaken. On that note, she reiterated the WHO’s support to Mauritius in accelerating the means to reach this target.

The 2021 Survey

The NCD survey 2021, the seventh one being carried out in Mauritius, was elaborated with the aim of defining appropriate intervention strategies to reduce disease and risk factor prevalence and to improve disease control.

The Survey sought to determine the prevalence and study the trends in the prevalence of Type 2 diabetes and Pre-diabetes; hypertension and cardiovascular disease; retinopathy and nephropathy; asthma and their associated risk factors. Persons aged between 20-74 years were the study’s participants. A total of 3,622 people responded positively to the survey.

Key findings:

The prevalence of diabetes since 1987 which stood at 14.3% has shown an increase till 2015, that is, denoting 22.9%. After which it has numerically decreased in the last 2021 survey, and now stands at 19.9%.

The standardised prevalence of Type 2 diabetes was 19.9%, with 21.6% among men and 18.5% among women. Most participants with diabetes were already known cases of diabetes, amounting to 14%, whilst the prevalence of newly diagnosed diabetes was 5.9%.

Furthermore, the survey indicates that following the new findings of the Mauritius NCD Survey 2021, as per the International Diabetes Federation, Mauritius is now ranked 12 th instead of 8 th .

Hypertension

Prevalence of hypertension stood at 27.2% (26.9% – men; and 27.5% – women).

Prevalence of overweight was 36%, with 38.7 % in men and 33.8% among women.

Obesity prevalence stood at 36.2%, 29.9% among men and 41.6% among women.

High cholesterol

Prevalence of High cholesterol stood at 34.8%, with 39.6% among men and 30.8% among women.

Smoking prevalence stood at 18.1%, with 35.3% among men and 3.7% among women.

Prevalence of asthma among adults stood at 7.5%, 6.8% among men and 8% among women.

Alcohol consumption

The prevalence of harmful alcohol consumption stood at 15.4%, 26.3% among men and 4.5% among women. Highest prevalence was seen among the 25-34 years age group (19%).

Diabetes prevalence (% of population ages 20 to 79) - Mauritius

Selected Countries and Economies

All countries and economies.

Privacy Notice
Access to Information

This site uses cookies to optimize functionality and give you the best possible experience. If you continue to navigate this website beyond this page, cookies will be placed on your browser. To learn more about cookies, click here.

RESEARCH DESIGN AND METHODS

Conclusions, acknowledgments, explaining the increase of diabetes prevalence and plasma glucose in mauritius.

Split-Screen
Article contents
Figures & tables
Supplementary Data
Peer Review
Open the PDF for in another window
Cite Icon Cite
Get Permissions

Dianna J. Magliano , Stefan Söderberg , Paul Z. Zimmet , Lei Chen , Noorjehan Joonas , Sudhir Kowlessur , Jose Larhubarbe , Dhanunjaye Gaoneadry , Vassen Pauvaday , Jaakko Tuomilehto , K. George M.M. Alberti , Jonathan E. Shaw; Explaining the Increase of Diabetes Prevalence and Plasma Glucose in Mauritius. Diabetes Care 1 January 2012; 35 (1): 87–91. https://doi.org/10.2337/dc11-0886

Download citation file:

Ris (Zotero)
Reference Manager

Secular trends in the epidemiology of diabetes are best described by studying the same population over time, but few such studies exist. Using surveys from Mauritius in 1987 and 2009, we examined 1 ) the change in the prevalence of diabetes, 2 ) the extent to which changes in traditional diabetes risk factors explained the increase, and 3 ) the change in the distribution of plasma glucose levels over time.

Independent population-based surveys were undertaken in Mauritius in 1987 and 2009 using similar methodology in adults aged 20–74 years. Physical measurements and fasting blood samples were taken, and an oral glucose tolerance test was performed at both surveys.

The age-standardized prevalence of diabetes in 2009 was 22.3% (95% CI 20.0–24.6) among men and 20.2% (18.3–22.3) among women, representing an increase since 1987 of 64 and 62% among men and women, respectively. Concurrent changes in the distribution of age, ethnicity, waist circumference, BMI, physical activity, smoking, family history of diabetes, and hypertension explained more of the increase in the prevalence of diabetes in men than in women. Increases in plasma glucose (especially fasting glucose) were seen across the population but were greater at the upper levels.

In Mauritius, there has been a marked increase in diabetes prevalence over 22 years. This mainly results from changes in traditional risk factors, leading to population-wide increases in plasma glucose levels. Interventions to control this escalation of diabetes should focus on population-wide strategies.

There are 285 million people with diabetes, and this is predicted to increase to 439 million by 2030, with larger increases in the developing than in the developed world ( 1 ). The reasons driving these increases include aging of populations, changes in physical activity levels, and patterns of dietary intake. Available data on the increasing prevalence of diabetes come from comparing studies of different designs in different populations, while most information on risk factors comes from cohort studies. Few publications to date have examined population-wide change in prevalence and in risk factors over time using consistent methodology at different time points.

Also unclear is to what extent the rise in the prevalence of diabetes is a result of a uniform increase across the whole spectrum of plasma glucose; the rise might be restricted to increases at the upper end of the glucose distribution. Data from the U.S. have shown that increases in the prevalence of diabetes have been accompanied by an increase in right skew of the fasting glucose distributions ( 2 ). There has been no detailed analysis of trends in the continuous distribution of glucose using 2-h postload glucose (2hrPG). Understanding the nature of the change in plasma glucose distribution is essential for determining the extent to which interventions should be directed at the whole population or just at those already at high risk.

Using national health surveys from Mauritius in 1987 and 2009, we sought to examine 1 ) the magnitude of the change in diabetes prevalence, 2 ) the extent to which changes in traditional diabetes risk factors can explain the increase in diabetes prevalence, and 3 ) whether increases in blood glucose levels were uniform across the population or were confined to those already at the upper end of the plasma glucose distribution.

Background and population

Mauritius has a population of 1.3 million people, which comprises 68% of South Asian (Indian) origin, 3% of Chinese, 27% of African (Creole), and 2% of Franco-Mauritian. In 1987 and 2009, population-based surveys using similar standardized protocols were conducted in Mauritius ( 3 , 4 ). In 1987, 11 randomly selected (with probability proportional to size) population clusters were surveyed. In this survey, all household members were invited to participate. In 2009, nine index clusters were chosen: one randomly selected from each of the nine districts in Mauritius. Then, two neighboring clusters were chosen surrounding the index cluster to form a “super cluster.” From this super cluster, one in three households was randomly selected and one adult per household was invited to participate.

A total of 11,454 people were recruited over the two surveys (1987, n = 5,083, age 22–74 years; 2009, n = 6371, age 20–74 years). The response rates for the 1987 ( 3 ) and the 2009 survey were both over 85%. The ethics committee of the Ministry of Health and Quality of Life, Mauritius, approved both surveys.

Risk factors at baseline

Weight, height, and waist and hip circumferences were measured. Waist circumference was measured using different methods in each survey but was adjusted accordingly based on previously published comparisons ( 5 ). BMI (measured as kilograms divided by the square of height in meters) was calculated from measured body weight and height. In both studies, a 75-g OGTT was undertaken. Blood was collected after an overnight fast of ≥8 h. Specimens for glucose were collected into fluoride/oxalate tubes, and glucose was assayed onsite using a YSI glucose analyzer (YSI, Yellow Springs, OH) instrument at both surveys. Glucose tolerance status was determined according to 1999 World Health Organization criteria ( 6 ). In 1987, blood pressure was measured with a standard mercury sphygmomanometer, while in 2009, blood pressure was measured using an automated blood pressure monitor (Omron Digital Auto Blood Pressure Monitor SEM-1).

Participants reporting a history of diabetes and using insulin or hypoglycemia medication or with FPG or 2hrPG in the diabetes range were defined as having diabetes. Diabetes case subjects almost exclusively had type 2 diabetes ( 7 ). For others, FPG <7.0 mmol/L and 2hrPG ≥7.8 but <11.1 mmol/L indicated impaired glucose tolerance (IGT), FPG 6.1–6.9 mmol/L and 2hrPG <7.8 mmol/L indicated impaired fasting glucose (IFG), and both FPG <6.1 mmol/L and 2hrPG <7.8 mmol/L indicated normal glucose tolerance. Hypertension was defined as mean systolic blood pressure ≥140 mmHg, mean diastolic blood pressure ≥90 mmHg, or treatment with antihypertensive medication.

In 1987 and 2009, similar interview-administered questionnaires were conducted to collect demographic, lifestyle, and medical history information including smoking, ethnicity, and family history of diabetes (first-degree family members with diabetes). Smoking was classified as current smokers, ex-smokers, or nonsmokers. Habitual physical activity was collected by different questionnaires in 1987 and 2009. In 1987, leisure and occupational physical activity was categorized on a four-level scale using questions developed specifically for the survey: sedentary (housebound), light (gardening and walking), moderate (e.g., aerobic activity 1–2 days per week), and heavy (e.g., aerobic activity ≥3 days per week), based on usual patterns over past years. In 2009, the General Physical Activity Questionnaire ( 8 ), which has been validated in an Indian population, was used to collect information about habitual physical activity. Because the measurement of physical activity was different at the two points, we applied a conservative approach to categorize 2009 participants into equivalent 1987 physical activity level categories. In brief, if participants in 2009 reported doing vigorous heavy physical activity for ≥3 days per week, they were categorized under the category “heavy” levels of physical activity and those reporting vigorous activity 1–2 days per week were categorized into the moderate level category. The remaining participants were classified into a combined category of sedentary and light physical activity.

Statistical analyses

Datasets from 1987 and 2009 were pooled for these analyses. Changes in the distribution of FPG and 2hrPG between surveys were illustrated by Tukey mean difference plots ( 9 ). In these plots, the difference between mean plasma glucose in the corresponding 12 quantiles of the two different surveys is plotted against the mean of the combined quantiles. Quantiles of 2hrPG that were outliers were excluded. To create the 12 quantiles, we used 2.5 and 97.5 percentiles as cut points to define the lowest and highest groups. The remaining population was divided into 10 groups with equal numbers. Polytomous multinominal logistic regression was used to test whether the glucose distributions were statistically different between the two surveys. For this, the 12 groups were created in the same way but all survey data were combined. These groups were used as the dependent outcome variable, and the effect of the year of survey was examined in crude models (year of survey only), followed by models adjusted by age, sex, ethnicity, waist circumference, hypertension, physical activity (occupational and leisure), and smoking.

Diabetes prevalence of both surveys was age and sex standardized to the 2008 population of Mauritius aged 22–74 years. Change in diabetes prevalence between the two cohorts was analyzed by logistic regression with diabetes status as the dependent outcome and year of survey as the independent outcome. The odds ratio (OR) of year of survey was computed in a crude model and after adjustment for the covariates as listed in the Tables.

To account for the clustered sampling design of the surveys, we used survey commands in the analyses. Stata (version 11.2; StataCorp, College Station, TX) was used for all analyses.

Table 1 shows that in 2009, participants had significantly larger BMI and waist and hip circumferences than in 1987 and were significantly older than in 1987. They were also significantly less likely to smoke but were more likely to have hypertension and to undertake leisure-time physical activity, though less likely to undertake physical activity at work.

Characteristics of survey populations in Mauritius

The age-standardized prevalence of diabetes, IGT, and IFG and by age group is shown in Table 2 . Between 1987 and 2009, the prevalence of diabetes increased by 64%, of IGT by 3%, and of IFG by 126%. Diabetes prevalence increased by ~64% in South Asians, ~63% in Africans, and ~17% in Chinese. IGT prevalence increased by 10% in South Asians and decreased by 12% in Africans and Chinese. The prevalence of IFG increase by 224% in South Asians and 39% in Africans and decreased by 2% in Chinese. The prevalence of diabetes significantly increased from 1987 to 2009 in all age groups except for those aged <34 years ( Table 2 ).

Prevalence of categories of abnormal glucose tolerance according to age-group and ethnicity

When 2hrPG was categorized into <7.8, 7.8–11.0, and ≥11.1 mmol/L independent of FPG, the proportion of those in the middle and upper groups increased between the two surveys by 19.5 and 14.4%, respectively. When FPG was categorized into <6.1, 6.1–6.9, and ≥7.0 mmol/L, the proportion in the middle and upper groups increased by 82 and 105%, respectively.

Among those with diabetes in both surveys, 14 and 35% were diagnosed, respectively, by their FPG and 2hrPG values only in 1987. In 2009, 22 and 28% were diagnosed by their FPG and 2hrPG only.

The geometric means of FPG and 2hrPG were significantly higher in 2009 than in 1987 after adjustment for age, sex, ethnicity, and waist circumference. With further adjustment for physical activity (leisure and occupational), smoking, and hypertension, mean FPG and 2hrPG remained significantly higher in 2009 than in 1987 but mean 2hrPG was slightly higher in 1987 than in 2009 ( P < 0.001) ( Table 3 ).

Geometric means and absolute changes in FPG and 2hrPG among Mauritian adults

Table 4 shows a series of logistic regression models that are adjusted with demographic, lifestyle, and biological risk factors to examine which of these groups of variables could explain the increase in prevalence of diabetes from 1987 to 2009. The ORs for diabetes were attenuated after the addition of demographic factors (model 2) and lifestyle-based factors (model 3). Adjusting for waist circumference and BMI attenuated the difference further (model 4), as did the addition of hypertension (model 5). The addition of family history of diabetes completely explained the difference in diabetes prevalence in the total population (OR 1.11 [95% CI 0.99–1.25]) and in men (1.03 [0.86–1.22]) but not in women (1.32 [1.13–1.55]).

ORs of having diabetes in 2009 compared with 1987

Supplementary Figure 1 (available in the Supplementary Data ) shows that FPG and 2hPG rose at all levels of blood glucose between 1987 and 2009. For FPG, the increase was greater at higher levels of blood glucose. With the use of crude polytomous multinominal logistic regression with FPG in 12 groups, this differential increase was significant for each of the 12 groups ( P < 0.001). When these models were adjusted for age, sex, ethnicity, waist circumference, hypertension, smoking, and family history of diabetes, the difference in distributions remained significant. Similar patterns were observed in men and women (data not shown). For 2hrPG, the rises were similar across the glucose distribution. In multinominal regression, the shift in glucose between years was significant for each of the 12 groups in crude models and remained significant after adjustment for sex, ethnicity, waist circumference, hypertension, smoking, and family history of diabetes ( P < 0.001). Similar patterns were observed in men and women (data not shown).

Our analyses show that diabetes prevalence has increased by 64% over 22 years in Mauritius. The increase was similar in men and women. The prevalence of IGT was relatively stable over the 22 years, while the prevalence of IFG increased by over 100%. The increase can be fully explained in men, but not in women, by a combination of differences in age distribution and modifiable (lifestyle) factors. The increase in diabetes prevalence is accompanied by significant increases in means of FPG and 2hrPG affecting the whole population, with a greater increase in FPG seen at the upper end of the FPG distribution.

The distribution of ethnicities in Mauritius reflects approximately two-thirds of the world population. Thus, epidemiological data from this nation’s population provides a global perspective and will be relevant in many settings.

The findings suggest that the majority of the change in prevalence is due to modifiable factors and is not due to selection bias, diagnostic bias, or change in mortality rates. The change in prevalence is unlikely due to selection bias for several reasons. Both surveys were sampled across randomly selected clusters with excellent response rates and similar survey methods. Diabetes prevalence most likely has not risen as a result of declining mortality rates because the increase in diabetes prevalence in younger ages (35–44 years), in which mortality has not changed significantly over time, was 68%.

We have shown that age and modifiable (lifestyle based) risk factors are responsible for all of the increase in diabetes prevalence in men and most of the increase in women. The greatest contributory factors were obesity and family history of diabetes. We cannot be certain, however, of precisely what information is captured by family history of diabetes. While it captures genetic risk of diabetes, family history probably represents more than this. One may argue that since diabetes was more common in 2009 than in 1987, family history of diabetes collected in 2009 could simply reflect this and therefore may be inappropriate to include as an explanatory variable. However, we believe that family history of diabetes may also be a good surrogate for measuring lifestyle and cultural factors such as diet. We were unable to directly measure the effect of a changing diet because methods were not directly comparable across the two surveys.

In 2010, Mauritius had the fourth highest prevalence of diabetes in the world ( 1 ). We show that diabetes prevalence increased by 64 and 62% among men and women, respectively, over 22 years. This increase was observed in those of South Asian and African descent but was much smaller in Chinese. Other studies describing an increase in diabetes prevalence over time are limited. A study of 60-year-old Danes showed that diabetes prevalence had increased by 38% over 22 years ( 10 ). The authors attribute all of the difference to a concurrent increase in BMI. The increase in diabetes prevalence in our study is higher than that reported in the total population of the Danish study, but the estimate of increase in diabetes prevalence was similar in men. There are some key differences between populations in both studies in terms of age range and ethnicity that prevent close comparisons. Unlike the Danish study, our study shows that the increase in diabetes over time was not due to concurrent increases in obesity alone. Our reports are similar to the change in prevalence observed in the U.S. using National Health and Nutrition Examination Survey (NHANES) data, which showed an increase in prevalence of diabetes of 51% over the period of 1988–1994 to 2005–2006 ( 11 ). More recently, a study in Scotland showed an increase of 80% in diabetes prevalence over the period of 2003–2008 ( 12 ).

The relative stability of the prevalence of IGT over time observed in this survey is consistent with our previous work in Mauritius documenting changes between 1987 and 1998. However, our findings of a doubling (and greater in some ethnicities) in the prevalence of IFG are not consistent with previous findings in Mauritius or in other countries. Using NHANES data, Cowie et al. ( 11 ) showed that the prevalence of IGT and the prevalence of IFG were relatively stable between 1988–1994 and 2005–2006.

We also found that although both 2hrPG and FPG increased over the time period, FPG increased more than 2hrPG and more participants developed diabetes via their FPG value versus their 2hrPG value. This is consistent with the hypothesis that obesity is more related to FPG than to 2hrPG (J. Tuomilehto et al., unpublished data). When 2hrPG and FPG glucose were examined in categories independent of each other, we found that the increase in the proportion of those in the IFG classes of 6.1–6.9 and ≥7.0 mmol/L was much larger than the increase in the proportion in the categories 7.8–11.1 and ≥11.1 mmol/L.

We were also able to explore the changing distribution of both FPG and 2hrPG. For both FPG and 2hrPG, all parts of the distribution showed increases in plasma glucose. The magnitude of the increase in glucose was larger at the upper than at the lower part of the glucose distribution. This was more obvious in the FPG distribution and less so with the 2hrPG distribution.

This study is not without limitations. Given the different instruments used to collect physical activity information, we were only able to account for physical activity using a crude method. This may have introduced some misclassification of physical activity status. Further, we were not able to assess the impact of changing dietary patterns on the increase in diabetes prevalence. Furthermore, we cannot rule out that the use of two different methods to measure blood pressure in the two surveys may have influenced our results.

The projections of diabetes for the coming years are modeled on expected changes in demographics and do not consider changes in modifiable risk factors such as obesity ( 1 ). Since much of the increase in diabetes prevalence can be explained by lifestyle factors, it is likely that these projections are an underestimate. The sheer magnitude of the increases in diabetes in 2009 in Mauritius portends a bleak future for Mauritians in terms of diabetes complications, associated disorders, and the costs of diabetes-related health care.

The 1987 survey was funded by the World Health Organization, Baker IDI Heart and Diabetes Institute, the University of Newcastle upon Tyne (Newcastle upon Tyne, U.K.), and the National Public Health Institute (Helsinki, Finland) and by National Institutes of Health Grant DK-25446. The 2009 survey was funded by the Ministry of Health and Quality of Life in Mauritius. S.S. is supported by grants from the Västerbotten County Council and the Swedish Heart and Lung Foundation. J.E.S. is supported by a National Health and Medical Research Council fellowship (586623). D.J.M. is supported by the Victorian Cancer Agency Public Health Fellowship.

No potential conflicts of interest relevant to this article were reported.

D.J.M. analyzed data, wrote the manuscript, and is the guarantor of this work. S.S., P.Z.Z., L.C., S.K., V.P., J.T., K.G.M.M.A., and J.E.S. reviewed and edited the manuscript. N.J., J.L., and D.G. collected and reviewed data.

We are most grateful to the Ministry of Health and Quality of Life in Mauritius for conducting both the baseline and the follow-up study. We also thank the participants for volunteering their time to be involved in the study. We also acknowledge Associate Professor Beverley Balkau from the Centre for Research in Epidemiology and Public Health, INSERM, for her statistical review of this work.

Supplementary data

Email alerts.

Online ISSN 1935-5548
Print ISSN 0149-5992
Diabetes Care
Clinical Diabetes
Diabetes Spectrum
Standards of Medical Care in Diabetes
Scientific Sessions Abstracts
BMJ Open Diabetes Research & Care
ShopDiabetes.org
ADA Professional Books

Clinical Compendia

Clinical Compendia Home
Latest News
DiabetesPro SmartBrief
Special Collections
DiabetesPro®
Diabetes Food Hub™
Insulin Affordability
Know Diabetes By Heart™
About the ADA
Journal Policies
For Reviewers
Advertising in ADA Journals
Reprints and Permission for Reuse
Copyright Notice/Public Access Policy
ADA Professional Membership
ADA Member Directory
Diabetes.org
X (Twitter)
Cookie Policy
Accessibility
Terms & Conditions
Get Adobe Acrobat Reader

This Feature Is Available To Subscribers Only

Key information

Mauritius is one of the 6 countries of the IDF SEA region. 537 million people have diabetes in the world and 90 million people in the SEA Region; by 2045 this will rise to 151.5 million.

Diabetes in Mauritius (2021)

943,400 Total adult population
26.5% prevalence of diabetes in adults
250,400 Total cases of diabetes in adults
--> --> -->