Nurse Researcher
Congruence between the Indian Diabetes Risk Score and Australian Type 2 Diabetes Risk Assessment tool screening in Asian-Indians Cite this article as: Fernandez R, Frost S (2013) Congruence between the Indian Diabetes Risk Score and Australian Type 2 Diabetes Risk Assessment tool screening in Asian-Indians. Nurse Researcher. 21, 2, 36-39. Date of submission: November 26 2012. Date of acceptance: February 26 2013. Correspondence to Ritin S Fernandez Email: [email protected]
Abstract
Ritin S Fernandez RN, MN, PhD is a professor of nursing at the University of Wollongong and St George Hospital, Australia
Aim To evaluate the performance of the simplified Indian Diabetes Risk Score (IDRS) and the Australian Type 2 Diabetes Risk Assessment (AUSDRISK) instruments in predicting diabetes in Indian-Australians.
Steven Frost MPubHealth is a lecturer at the University of Western Sydney, Australia Peer review This article has been subject to double-blind review and checked using antiplagiarism software Author guidelines http://nr.rcnpublishing.com
Background Screening for diabetes in the general community is common and numerous scoring systems are being used to predict the risk of diabetes. Data sources For this cross-sectional study, data were obtained from people attending the Australia India Friendship Fair. Review methods Data relating to risk factors for diabetes were obtained using a questionnaire and a random blood glucose level. The IDRS and AUSDRISK scores were calculated. Student’s t-test, Pearson chi-square, and receiver-operating characteristic
Introduction The prevalence of diabetes, particularly type 2 diabetes, among Asian-Indians continues to increase (Danaei et al 2011). According to the World Health Organization (WHO) Statistical Information Systems, the age-standardised mortality rate due to diabetes among Indians is 22.4 per 100,000, which is significantly higher than the rates of other countries (WHO 2011a). Diabetes places an enormous burden on the individual and the community because of the resulting physical and psychosocial disabilities (Australian Institute of Health and Welfare 2008, 36 November 2013 | Volume 21 | Number 2
36-39 NR Nov 2013.indd 36
curves were used to compare the performance of the predictive scores. Discussion Data were analysed for 136 participants: 28 per cent of individuals considered to be low-risk and 35 per cent considered to be moderate-risk according to AUSDRISK were classified as moderate-risk and high-risk respectively by IDRS. Conclusion The two models were not congruent in predicting diabetes risk among Asian-Indians. Implications for practice/research The results of this study have significant implications for education relating to diabetes screening. Key words Assessment tool, risk, diabetes mellitus, screening, Asian-Indians
WHO 2011b), as well as creating significant costs for healthcare systems (Walsh et al 2005). In the past 50 years, there has been a significant growth among Asian-Indians in migration rates to developed countries including Australia (Productivity Commission 2010), the UK (Office for National Statistics 2012) and the United States (Shrestha and Heisler 2011). There are more than 30 million Asian-Indians living outside India, not including the large number of students overseas (United Nations Development Programme 2010). © RCN PUBLISHING / NURSE RESEARCHER
Downloaded from RCNi.com by ${individualUser.displayName} on Nov 15, 2015. For personal use only. No other uses without permission. Copyright © 2015 RCNi Ltd. All rights reserved.
24/10/2013 14:20
Risk prediction Given that diabetes represents an important health problem among Asian-Indians, assessing the risk for diabetes among this population is vital. Screening is generally undertaken to identify asymptomatic individuals who are likely to have diabetes. In the past decade, numerous scoring systems for assessing the risk of developing diabetes have been developed (Lin et al 2009, Collins et al 2011). These instruments are widely used by nurses in health promotion activities and by GPs to assess the risk of diabetes and begin early interventions for those at risk (Diabetes Australia 2011). In countries with multicultural populations, it is important to select appropriate screening tools for diabetes. The aim of this study was therefore to evaluate the performance of two instruments: the Indian Diabetes Risk Score (IDRS) and the Australian Type 2 Diabetes Risk Assessment (AUSDRISK), in predicting the risk of developing diabetes among Asian-Indians living in Australia.
Indian Diabetes Risk Score The simplified IDRS was developed and validated in a cohort of 2,350 participants recruited for the Chennai urban rural epidemiology study in India (Mohan et al 2005). It is used to identify undiagnosed diabetics using four risk factors: age, abdominal obesity, family history of diabetes and physical activity. The total risk score is determined by adding the scores for each risk factor. The minimum score obtainable is 0 and the maximum 100, with higher scores representing greater risk of diabetes. The instrument has been reported to demonstrate good discrimination properties. The area under the receiver-operating curve (AROC) was 0.698 (95 per cent confidence interval (CI): 0.663-0.733). The sensitivity, specificity and positive predictive value (PPV) for identifying incident diabetes at an IDRS value of 60 or more were 72.5 per cent, 60.1 per cent and 17.0 per cent respectively (Mohan et al 2005).
Australian Type 2 Diabetes Risk Assessment AUSDRISK was developed from the Australian diabetes obesity and lifestyle study, which involved 6,000 adults (Chen et al 2010). It was validated in two other Australian studies (Grant et al 2006, Cugati et al 2007). AUSDRISK predicts a five-year risk of diabetes using nine risk factors: age, gender, ethnicity, parental history of diabetes, history of high blood-glucose level, use of antihypertensive medications, smoking, physical inactivity and waist circumference. The total five-year risk score is obtained by adding the scores for each risk © RCN PUBLISHING / NURSE RESEARCHER
36-39 NR Nov 2013.indd 37
factor. The minimum score obtainable is 0 and the maximum 36, with higher scores representing greater risk of diabetes. The AROC of AUSDRISK was 0.78 (95 per cent CI: 0.76-0.81). The optimum sensitivity (74.0 per cent), specificity (67.7 per cent) and PPV (12.7 per cent) for determining a five-year risk of diabetes were obtained at an AUSDRISK score of 12 or more. Both risk assessment tools are based on statistical modelling techniques for selecting and weighing the variables, and the computation of the risk of developing diabetes is based on a logistic regression technique.
Method Design and setting This cross-sectional study used a convenience sampling technique. The setting for the study was the health promotion stall at the Australia India Friendship Fair in Sydney, Australia, in 2010. Participants The Australia India Friendship Fair is an annual event conducted by the United Indian Association. High attendance numbers of Asian-Indians have been reported at these fairs. Participants for this study were people who attended the health promotion stall at the fair and who said they were of Asian-Indian origin, aged between 18 and 80, and able to speak English. They were told about the study by a research assistant and given an information sheet. All participants gave their written consent to be included. They were informed that all data collected would be kept confidential and that only the principal researchers would have access to their details. The University of Western Sydney human research ethics committee granted ethics approval. Unique numeric identifiers and password-protected files were used to maintain participants’ privacy and confidentiality. Data collection Participants were offered health promotion screening, which included the assessment of risk factors for diabetes using a self-administered questionnaire and the obtaining of a random blood-glucose level (BGL). The questionnaire consisted of items relating to demographic details (age, gender, level of education, marital status) and whether they had a family history or personal history of diabetes. A portable sensor was used to obtain a random whole-BGL from a capillary (finger stick) sample from each participant. The likelihood of having diabetes was considered if the random BGL was greater than 11.1mmol/l (200mg/dl) (Diabetes Australia 2011). Data analysis Risk scores for diabetes were calculated from each participant’s questionnaire November 2013 | Volume 21 | Number 2 37
Downloaded from RCNi.com by ${individualUser.displayName} on Nov 15, 2015. For personal use only. No other uses without permission. Copyright © 2015 RCNi Ltd. All rights reserved.
24/10/2013 14:20
Nurse Researcher Table 1
Demographic characteristics of participants (n=49) Values
Variable Mean age (years)
46 (range: 18 to 77)
Gender Male
53
Female
83
Educational level Less than year 10
3
High school certificate
13
Bachelor’s degree
54
Master’s degree
43
Doctorate
4
using AUSDRISK and IDRS. The correlation between risk scores was assessed using Spearman’s rank correlation coefficient. Further to this, individuals were categorised into the three levels of risk of diabetes based on PPVs of 0-12 per cent, 12-25 per cent and 25 per cent or above that were provided in the tools’ original Figure 1 Comparison of IDRS and AUSDRISK
Australian Type 2 Risk Assessment tool score
25 -
Line of agreement Line of regression Pearson’s correlation coefficient
20 -
15 -
10 -
5-
0-
20
40
60
Indian Diabetes Risk Score 38 November 2013 | Volume 21 | Number 2
36-39 NR Nov 2013.indd 38
80
100
papers (Mohan et al 2005, Chen et al 2010). Men and women with histories of diabetes were excluded from our final analysis.
Results The average age of the 136 participants (83 women and 53 men) included in the analysis was 46 (range: 18 to 77) and most participants had tertiary qualifications (Table 1). The mean risk scores for IDRS and AUSDRISK were 48 (standard deviation (SD): 20) and 13 (SD: 5) respectively. Using random BGL≥10.0mmol/l (180mg/dl), no statistically significant difference in the area under the curve was observed from IDRS’s 0.72 (0.56-0.88) and AUSDRISK’s 0.75 (0.60-0.90) (p=0.61). The correlation between IDRS’s and AUSDRISK’s risk scores was 0.77 (95 per cent CI: 0.69-0.83) (Figure 1). When categorised according to the risk of diabetes, based on PPVs from the original published papers, 28 per cent of individuals considered to be at low risk by AUSDRISK were classified as being at moderate risk by IDRS and 35 per cent of individuals considered to be at moderate risk by AUSDRISK were classified as high risk by IDRS (Table 2).
Discussion Given the high risk of diabetes among Asian-Indians, screening for the disease in this vulnerable population is paramount. Therefore, it is important to use an appropriate screening tool to avoid misidentifying patients with diabetes. In this study, a significant positive correlation between IDRS and AUSDRISK was identified. However, there was a significant difference in the classification of individual risks of diabetes. Importantly, we found that one in five people identified by IDRS for further screening for diabetes may be missed by AUSDRISK and so may not be referred for further investigation. These results were obtained despite the fact that we allocated minimal scores in the IDRS for family history, as we did not ask if both parents of the participant had diabetes. This finding has urgent implications for the education of healthcare professionals in using instruments to predict diabetes risk in various populations. First, education should be provided to make healthcare professionals aware of the probability of diabetes in the given population. Among AsianIndians, the probability of developing diabetes is high (WHO 2011a), so it is unsurprising that a proportion of individuals were misclassified. Misclassification of individuals at risk of diabetes has a significant effect on patient outcomes in terms of premature mortality © RCN PUBLISHING / NURSE RESEARCHER
Downloaded from RCNi.com by ${individualUser.displayName} on Nov 15, 2015. For personal use only. No other uses without permission. Copyright © 2015 RCNi Ltd. All rights reserved.
24/10/2013 14:20
Risk prediction Table 2
Comparison of IDRS and AUSDRISK scores in the study
Indian Diabetes Risk Score
Australian Type 2 Diabetes Risk Assessment
Total
0-11
12-17
18+
0-39
36 (100%)
0
0
36
40-79
23 (27.7%)
57 (68.7%)
3 (3.6%)
83
80-100
0
6 (35.3%)
11 (64.7%)
17
Total
59
63
14
136
(Bertoni et al 2004) from cardiovascular disease, kidney failure and peripheral vascular complications (Fowler 2008). Although our study was limited by the relatively small sample size, the results highlight the importance of education relating to health professionals’ use of appropriate instruments for risk prediction among diverse populations. Indiscriminate and improper use of diabetes risk assessment instruments across diverse groups may result in the misclassification of individuals. Another limitation
Conflict of interest None declared
of our study was that we did not undertake an oral glucose tolerance test, which could have provided robust information. Future research should compare IDRS with screening tools developed for the general population in the country in question.
Conclusion The two models were not congruent in predicting diabetes risk among Asian-Indians. The results of this study have significant implications for education relating to diabetes screening.
Acknowledgements The authors would like to thank the individuals who participated in the study, as well as the Australia India Medical Graduate Association, which assisted with data collection for this study
Online archive For related information, visit our online archive and search using the keywords
References Australian Institute of Health and Welfare (2008) Australia’s Health 2008. Canberra Australian Institute of Health and Welfare, Canberra ACT. Bertoni AG, Kirk JK, Goff DC et al (2004) Excess mortality related to diabetes mellitus in elderly Medicare beneficiaries. Annals of Epidemiology. 14, 5, 362-367. Chen L, Magliano DJ, Balkau B et al (2010) AUSDRISK: an Australian Type 2 Diabetes Risk Assessment Tool based on demographic, lifestyle and simple anthropometric measures. The Medical Journal of Australia. 192, 4, 197-202. Collins GS, Mallett S, Omar O et al (2011) Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Medicine. 9, 103. Cugati S, Wang JJ, Rochtchina E et al (2007) Tenyear incidence of diabetes in older Australians: the Blue Mountains Eye Study. The Medical Journal of Australia. 186, 3, 131-135.
Danaei G, Finucane MM, Lu Y et al (2011) National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2.7 million participants. Lancet. 378, 9785, 31-40. Diabetes Australia (2010) National Policy Priorities 2010: Better Management and Prevention of Diabetes For All Australians. Diabetes Australia, Canberra ACT. Diabetes Australia (2011) Diabetes Management In General Practice: Guidelines For Type 2 Diabetes 2011/12. Diabetes Australia, Canberra ACT. Fowler MJ (2008) Microvascular and macrovascular complications of diabetes. Clinical Diabetes. 26, 2, 77-82. Grant JF, Chittleborough CR, Taylor AW et al (2006) The North West Adelaide Health Study: detailed methods and baseline segmentation
© RCN PUBLISHING / NURSE RESEARCHER
36-39 NR Nov 2013.indd 39
of a cohort for selected chronic diseases. Epidemiological Perspectives and Innovations. 3, 4. Lin JW, Chang YC, Li HY et al (2009) Crosssectional validation of diabetes risk scores for predicting diabetes, metabolic syndrome, and chronic kidney disease in Taiwanese. Diabetes Care. 32, 12, 2294-2296. Mohan V, Deepa R, Deepa M et al (2005) A simplified Indian Diabetes Risk Score for screening for undiagnosed diabetic subjects. The Journal of the Association of Physicians of India. 53, 759-763. Office for National Statistics (2012) Long-Term International Migration – 2011. Office for National Statistics, London. Productivity Commission (2010) Population and Migration: Understanding the Numbers. www.pc.gov.au/research/commission/ population-migration (Last accessed: August 19 2013.)
Shrestha LB, Heisler EJ (2011) The Changing Demographic Profile of the United States. Congressional Research Service, Washington DC. United Nations Development Programme (2010) Human Development Report 2010. UNDP, New York NY. Walsh MG, Zgibor J, Songer T et al (2005) The socioeconomic correlates of global complication prevalence in type 1 diabetes (T1D): a multinational comparison. Diabetes Research and Clinical Practice. 70, 2, 143-150. World Health Organization (2011a) World Health Statistics 2011. WHO, Geneva, Switzerland. World Health Organization (2011b) Global Status Report on Noncommunicable Diseases 2010. WHO, Geneva, Switzerland.
November 2013 | Volume 21 | Number 2 39
Downloaded from RCNi.com by ${individualUser.displayName} on Nov 15, 2015. For personal use only. No other uses without permission. Copyright © 2015 RCNi Ltd. All rights reserved.
24/10/2013 14:20
Congruence between the Indian Diabetes Risk Score and Australian Type 2 Diabetes Risk Assessment tool screening in Asian-Indians Cite this article as: Fernandez R, Frost S (2013) Congruence between the Indian Diabetes Risk Score and Australian Type 2 Diabetes Risk Assessment tool screening in Asian-Indians. Nurse Researcher. 21, 2, 36-39. Date of submission: November 26 2012. Date of acceptance: February 26 2013. Correspondence to Ritin S Fernandez Email: [email protected]
Abstract
Ritin S Fernandez RN, MN, PhD is a professor of nursing at the University of Wollongong and St George Hospital, Australia
Aim To evaluate the performance of the simplified Indian Diabetes Risk Score (IDRS) and the Australian Type 2 Diabetes Risk Assessment (AUSDRISK) instruments in predicting diabetes in Indian-Australians.
Steven Frost MPubHealth is a lecturer at the University of Western Sydney, Australia Peer review This article has been subject to double-blind review and checked using antiplagiarism software Author guidelines http://nr.rcnpublishing.com
Background Screening for diabetes in the general community is common and numerous scoring systems are being used to predict the risk of diabetes. Data sources For this cross-sectional study, data were obtained from people attending the Australia India Friendship Fair. Review methods Data relating to risk factors for diabetes were obtained using a questionnaire and a random blood glucose level. The IDRS and AUSDRISK scores were calculated. Student’s t-test, Pearson chi-square, and receiver-operating characteristic
Introduction The prevalence of diabetes, particularly type 2 diabetes, among Asian-Indians continues to increase (Danaei et al 2011). According to the World Health Organization (WHO) Statistical Information Systems, the age-standardised mortality rate due to diabetes among Indians is 22.4 per 100,000, which is significantly higher than the rates of other countries (WHO 2011a). Diabetes places an enormous burden on the individual and the community because of the resulting physical and psychosocial disabilities (Australian Institute of Health and Welfare 2008, 36 November 2013 | Volume 21 | Number 2
36-39 NR Nov 2013.indd 36
curves were used to compare the performance of the predictive scores. Discussion Data were analysed for 136 participants: 28 per cent of individuals considered to be low-risk and 35 per cent considered to be moderate-risk according to AUSDRISK were classified as moderate-risk and high-risk respectively by IDRS. Conclusion The two models were not congruent in predicting diabetes risk among Asian-Indians. Implications for practice/research The results of this study have significant implications for education relating to diabetes screening. Key words Assessment tool, risk, diabetes mellitus, screening, Asian-Indians
WHO 2011b), as well as creating significant costs for healthcare systems (Walsh et al 2005). In the past 50 years, there has been a significant growth among Asian-Indians in migration rates to developed countries including Australia (Productivity Commission 2010), the UK (Office for National Statistics 2012) and the United States (Shrestha and Heisler 2011). There are more than 30 million Asian-Indians living outside India, not including the large number of students overseas (United Nations Development Programme 2010). © RCN PUBLISHING / NURSE RESEARCHER
Downloaded from RCNi.com by ${individualUser.displayName} on Nov 15, 2015. For personal use only. No other uses without permission. Copyright © 2015 RCNi Ltd. All rights reserved.
24/10/2013 14:20
Risk prediction Given that diabetes represents an important health problem among Asian-Indians, assessing the risk for diabetes among this population is vital. Screening is generally undertaken to identify asymptomatic individuals who are likely to have diabetes. In the past decade, numerous scoring systems for assessing the risk of developing diabetes have been developed (Lin et al 2009, Collins et al 2011). These instruments are widely used by nurses in health promotion activities and by GPs to assess the risk of diabetes and begin early interventions for those at risk (Diabetes Australia 2011). In countries with multicultural populations, it is important to select appropriate screening tools for diabetes. The aim of this study was therefore to evaluate the performance of two instruments: the Indian Diabetes Risk Score (IDRS) and the Australian Type 2 Diabetes Risk Assessment (AUSDRISK), in predicting the risk of developing diabetes among Asian-Indians living in Australia.
Indian Diabetes Risk Score The simplified IDRS was developed and validated in a cohort of 2,350 participants recruited for the Chennai urban rural epidemiology study in India (Mohan et al 2005). It is used to identify undiagnosed diabetics using four risk factors: age, abdominal obesity, family history of diabetes and physical activity. The total risk score is determined by adding the scores for each risk factor. The minimum score obtainable is 0 and the maximum 100, with higher scores representing greater risk of diabetes. The instrument has been reported to demonstrate good discrimination properties. The area under the receiver-operating curve (AROC) was 0.698 (95 per cent confidence interval (CI): 0.663-0.733). The sensitivity, specificity and positive predictive value (PPV) for identifying incident diabetes at an IDRS value of 60 or more were 72.5 per cent, 60.1 per cent and 17.0 per cent respectively (Mohan et al 2005).
Australian Type 2 Diabetes Risk Assessment AUSDRISK was developed from the Australian diabetes obesity and lifestyle study, which involved 6,000 adults (Chen et al 2010). It was validated in two other Australian studies (Grant et al 2006, Cugati et al 2007). AUSDRISK predicts a five-year risk of diabetes using nine risk factors: age, gender, ethnicity, parental history of diabetes, history of high blood-glucose level, use of antihypertensive medications, smoking, physical inactivity and waist circumference. The total five-year risk score is obtained by adding the scores for each risk © RCN PUBLISHING / NURSE RESEARCHER
36-39 NR Nov 2013.indd 37
factor. The minimum score obtainable is 0 and the maximum 36, with higher scores representing greater risk of diabetes. The AROC of AUSDRISK was 0.78 (95 per cent CI: 0.76-0.81). The optimum sensitivity (74.0 per cent), specificity (67.7 per cent) and PPV (12.7 per cent) for determining a five-year risk of diabetes were obtained at an AUSDRISK score of 12 or more. Both risk assessment tools are based on statistical modelling techniques for selecting and weighing the variables, and the computation of the risk of developing diabetes is based on a logistic regression technique.
Method Design and setting This cross-sectional study used a convenience sampling technique. The setting for the study was the health promotion stall at the Australia India Friendship Fair in Sydney, Australia, in 2010. Participants The Australia India Friendship Fair is an annual event conducted by the United Indian Association. High attendance numbers of Asian-Indians have been reported at these fairs. Participants for this study were people who attended the health promotion stall at the fair and who said they were of Asian-Indian origin, aged between 18 and 80, and able to speak English. They were told about the study by a research assistant and given an information sheet. All participants gave their written consent to be included. They were informed that all data collected would be kept confidential and that only the principal researchers would have access to their details. The University of Western Sydney human research ethics committee granted ethics approval. Unique numeric identifiers and password-protected files were used to maintain participants’ privacy and confidentiality. Data collection Participants were offered health promotion screening, which included the assessment of risk factors for diabetes using a self-administered questionnaire and the obtaining of a random blood-glucose level (BGL). The questionnaire consisted of items relating to demographic details (age, gender, level of education, marital status) and whether they had a family history or personal history of diabetes. A portable sensor was used to obtain a random whole-BGL from a capillary (finger stick) sample from each participant. The likelihood of having diabetes was considered if the random BGL was greater than 11.1mmol/l (200mg/dl) (Diabetes Australia 2011). Data analysis Risk scores for diabetes were calculated from each participant’s questionnaire November 2013 | Volume 21 | Number 2 37
Downloaded from RCNi.com by ${individualUser.displayName} on Nov 15, 2015. For personal use only. No other uses without permission. Copyright © 2015 RCNi Ltd. All rights reserved.
24/10/2013 14:20
Nurse Researcher Table 1
Demographic characteristics of participants (n=49) Values
Variable Mean age (years)
46 (range: 18 to 77)
Gender Male
53
Female
83
Educational level Less than year 10
3
High school certificate
13
Bachelor’s degree
54
Master’s degree
43
Doctorate
4
using AUSDRISK and IDRS. The correlation between risk scores was assessed using Spearman’s rank correlation coefficient. Further to this, individuals were categorised into the three levels of risk of diabetes based on PPVs of 0-12 per cent, 12-25 per cent and 25 per cent or above that were provided in the tools’ original Figure 1 Comparison of IDRS and AUSDRISK
Australian Type 2 Risk Assessment tool score
25 -
Line of agreement Line of regression Pearson’s correlation coefficient
20 -
15 -
10 -
5-
0-
20
40
60
Indian Diabetes Risk Score 38 November 2013 | Volume 21 | Number 2
36-39 NR Nov 2013.indd 38
80
100
papers (Mohan et al 2005, Chen et al 2010). Men and women with histories of diabetes were excluded from our final analysis.
Results The average age of the 136 participants (83 women and 53 men) included in the analysis was 46 (range: 18 to 77) and most participants had tertiary qualifications (Table 1). The mean risk scores for IDRS and AUSDRISK were 48 (standard deviation (SD): 20) and 13 (SD: 5) respectively. Using random BGL≥10.0mmol/l (180mg/dl), no statistically significant difference in the area under the curve was observed from IDRS’s 0.72 (0.56-0.88) and AUSDRISK’s 0.75 (0.60-0.90) (p=0.61). The correlation between IDRS’s and AUSDRISK’s risk scores was 0.77 (95 per cent CI: 0.69-0.83) (Figure 1). When categorised according to the risk of diabetes, based on PPVs from the original published papers, 28 per cent of individuals considered to be at low risk by AUSDRISK were classified as being at moderate risk by IDRS and 35 per cent of individuals considered to be at moderate risk by AUSDRISK were classified as high risk by IDRS (Table 2).
Discussion Given the high risk of diabetes among Asian-Indians, screening for the disease in this vulnerable population is paramount. Therefore, it is important to use an appropriate screening tool to avoid misidentifying patients with diabetes. In this study, a significant positive correlation between IDRS and AUSDRISK was identified. However, there was a significant difference in the classification of individual risks of diabetes. Importantly, we found that one in five people identified by IDRS for further screening for diabetes may be missed by AUSDRISK and so may not be referred for further investigation. These results were obtained despite the fact that we allocated minimal scores in the IDRS for family history, as we did not ask if both parents of the participant had diabetes. This finding has urgent implications for the education of healthcare professionals in using instruments to predict diabetes risk in various populations. First, education should be provided to make healthcare professionals aware of the probability of diabetes in the given population. Among AsianIndians, the probability of developing diabetes is high (WHO 2011a), so it is unsurprising that a proportion of individuals were misclassified. Misclassification of individuals at risk of diabetes has a significant effect on patient outcomes in terms of premature mortality © RCN PUBLISHING / NURSE RESEARCHER
Downloaded from RCNi.com by ${individualUser.displayName} on Nov 15, 2015. For personal use only. No other uses without permission. Copyright © 2015 RCNi Ltd. All rights reserved.
24/10/2013 14:20
Risk prediction Table 2
Comparison of IDRS and AUSDRISK scores in the study
Indian Diabetes Risk Score
Australian Type 2 Diabetes Risk Assessment
Total
0-11
12-17
18+
0-39
36 (100%)
0
0
36
40-79
23 (27.7%)
57 (68.7%)
3 (3.6%)
83
80-100
0
6 (35.3%)
11 (64.7%)
17
Total
59
63
14
136
(Bertoni et al 2004) from cardiovascular disease, kidney failure and peripheral vascular complications (Fowler 2008). Although our study was limited by the relatively small sample size, the results highlight the importance of education relating to health professionals’ use of appropriate instruments for risk prediction among diverse populations. Indiscriminate and improper use of diabetes risk assessment instruments across diverse groups may result in the misclassification of individuals. Another limitation
Conflict of interest None declared
of our study was that we did not undertake an oral glucose tolerance test, which could have provided robust information. Future research should compare IDRS with screening tools developed for the general population in the country in question.
Conclusion The two models were not congruent in predicting diabetes risk among Asian-Indians. The results of this study have significant implications for education relating to diabetes screening.
Acknowledgements The authors would like to thank the individuals who participated in the study, as well as the Australia India Medical Graduate Association, which assisted with data collection for this study
Online archive For related information, visit our online archive and search using the keywords
References Australian Institute of Health and Welfare (2008) Australia’s Health 2008. Canberra Australian Institute of Health and Welfare, Canberra ACT. Bertoni AG, Kirk JK, Goff DC et al (2004) Excess mortality related to diabetes mellitus in elderly Medicare beneficiaries. Annals of Epidemiology. 14, 5, 362-367. Chen L, Magliano DJ, Balkau B et al (2010) AUSDRISK: an Australian Type 2 Diabetes Risk Assessment Tool based on demographic, lifestyle and simple anthropometric measures. The Medical Journal of Australia. 192, 4, 197-202. Collins GS, Mallett S, Omar O et al (2011) Developing risk prediction models for type 2 diabetes: a systematic review of methodology and reporting. BMC Medicine. 9, 103. Cugati S, Wang JJ, Rochtchina E et al (2007) Tenyear incidence of diabetes in older Australians: the Blue Mountains Eye Study. The Medical Journal of Australia. 186, 3, 131-135.
Danaei G, Finucane MM, Lu Y et al (2011) National, regional, and global trends in fasting plasma glucose and diabetes prevalence since 1980: systematic analysis of health examination surveys and epidemiological studies with 370 country-years and 2.7 million participants. Lancet. 378, 9785, 31-40. Diabetes Australia (2010) National Policy Priorities 2010: Better Management and Prevention of Diabetes For All Australians. Diabetes Australia, Canberra ACT. Diabetes Australia (2011) Diabetes Management In General Practice: Guidelines For Type 2 Diabetes 2011/12. Diabetes Australia, Canberra ACT. Fowler MJ (2008) Microvascular and macrovascular complications of diabetes. Clinical Diabetes. 26, 2, 77-82. Grant JF, Chittleborough CR, Taylor AW et al (2006) The North West Adelaide Health Study: detailed methods and baseline segmentation
© RCN PUBLISHING / NURSE RESEARCHER
36-39 NR Nov 2013.indd 39
of a cohort for selected chronic diseases. Epidemiological Perspectives and Innovations. 3, 4. Lin JW, Chang YC, Li HY et al (2009) Crosssectional validation of diabetes risk scores for predicting diabetes, metabolic syndrome, and chronic kidney disease in Taiwanese. Diabetes Care. 32, 12, 2294-2296. Mohan V, Deepa R, Deepa M et al (2005) A simplified Indian Diabetes Risk Score for screening for undiagnosed diabetic subjects. The Journal of the Association of Physicians of India. 53, 759-763. Office for National Statistics (2012) Long-Term International Migration – 2011. Office for National Statistics, London. Productivity Commission (2010) Population and Migration: Understanding the Numbers. www.pc.gov.au/research/commission/ population-migration (Last accessed: August 19 2013.)
Shrestha LB, Heisler EJ (2011) The Changing Demographic Profile of the United States. Congressional Research Service, Washington DC. United Nations Development Programme (2010) Human Development Report 2010. UNDP, New York NY. Walsh MG, Zgibor J, Songer T et al (2005) The socioeconomic correlates of global complication prevalence in type 1 diabetes (T1D): a multinational comparison. Diabetes Research and Clinical Practice. 70, 2, 143-150. World Health Organization (2011a) World Health Statistics 2011. WHO, Geneva, Switzerland. World Health Organization (2011b) Global Status Report on Noncommunicable Diseases 2010. WHO, Geneva, Switzerland.
November 2013 | Volume 21 | Number 2 39
Downloaded from RCNi.com by ${individualUser.displayName} on Nov 15, 2015. For personal use only. No other uses without permission. Copyright © 2015 RCNi Ltd. All rights reserved.
24/10/2013 14:20
