|Year : 2020 | Volume
| Issue : 4 | Page : 207-210
Development and validation of composite risk score to assess risks of major noncommunicable diseases in Northern Indian populations: A research protocol
Ria Nangia1, JS Thakur1, Anil Kumar Bhalla2, Ajay Duseja3
1 Department of Community Medicine and School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh, India
2 Advanced Paediatric Centre, Post Graduate Institute of Medical Education and Research, Chandigarh, India
3 Department of Hepatology, Post Graduate Institute of Medical Education and Research, Chandigarh, India
|Date of Submission||04-May-2019|
|Date of Decision||18-May-2020|
|Date of Acceptance||20-Jul-2020|
|Date of Web Publication||31-Dec-2020|
Ms. Ria Nangia
Department of Community Medicine and School of Public Health, Post Graduate Institute of Medical Education and Research, Chandigarh
Source of Support: None, Conflict of Interest: None
Background: Noncommunicable diseases (NCDs) which mainly consist of cardiovascular diseases, diabetes, cancer, and chronic respiratory diseases account for 38 million deaths out of the 56 million deaths globally and 54% of healthy life years lost in 2012. Risk scores predict the risk of the disease efficiently, and an important strategy to prevent or delay the occurrence of chronic diseases is the early identification of those with undiagnosed NCDs.
Methods: Two different population-based studies will be used for the development of the score and its validation. Secondary data from WHO STEPS Survey Haryana of 5250 individuals will be used to develop the predictive score and will be validated using STEPS Punjab data. Risk predictors will be used as the independent factors in the multivariate logistic regression and the dependent variable will be the disease. The regression coefficient will be used to assign each variable category a score. Risk score will be derived from receiver operating characteristic curve and the optimal cut-off will be determined using Youden's Index. Measures of overall predictive accuracy, discrimination and calibration will be used to assess predictive performance.
Discussion: The present study is an attempt to develop a composite risk score for major NCDs so that effective preventive and control measures can be initiated once the risk is known. The developed tool will help in risk assessment and the successful implementation of NCD programs.
Keywords: Chronic diseases, integrated risk score, noncommunicable diseases, risk factors, risk score
|How to cite this article:|
Nangia R, Thakur J S, Bhalla AK, Duseja A. Development and validation of composite risk score to assess risks of major noncommunicable diseases in Northern Indian populations: A research protocol. Int J Non-Commun Dis 2020;5:207-10
|How to cite this URL:|
Nangia R, Thakur J S, Bhalla AK, Duseja A. Development and validation of composite risk score to assess risks of major noncommunicable diseases in Northern Indian populations: A research protocol. Int J Non-Commun Dis [serial online] 2020 [cited 2021 Apr 22];5:207-10. Available from: https://www.ijncd.org/text.asp?2020/5/4/207/305994
| Introduction|| |
Annually, 80% of noncommunicable disease (NCD) deaths and 90% of premature NCD deaths occur in low- and middle-income countries (LMICs). Studies suggest that NCDs will account for 69% of all global deaths by 2030 with 80% of the deaths in LMICs. NCDs are increasingly common and create a substantial burden of suffering and cost. The burden of NCDs is rising rapidly and has now become a major public health concern and is a challenge to both national and international development, and as a developing country, India inevitably faces an increased prevalence of NCDs. Maximum of the patients with chronic diseases are undiagnosed, and the delay from disease onset to diagnosis may exceed 10 years. The cost of treating NCDs and in particular, dealing with its complications, is a major concern, especially in developing countries such as the Indian subcontinent where the prevalence of lifestyle diseases is high. It has been suggested that screening could be one approach to reduce the burden of morbidity attributable to NCDs, however universal screening of all adults in the population is not practical and is not recommended.
Risk assessment tools facilitate the translation of these risk models to estimate an individual's likelihood of developing different NCDs by assessing the combination of risk factors including environmental and behavioral risk factors. In the great majority of cases, an individual's risk is the product of multiple risk factors, and there is a need for an absolute risk estimation to be made for individuals believed to be at risk who have not presented as high risk by the presence of established disease.
There are many individual risk scores for different chronic diseases as for cancer,,,,, stroke,,, cardiovascular diseases (CVDs),,, diabetes,,, and nonalcoholic fatty liver disease,,, chronic obstructive pulmonary disease, etc.,,, Although varied individual risk scores are developed for different NCDs, a composite risk score to assess the risk of the disease is not available despite the fact that they share common risk factors. It is imperative that an individual at high risk of NCDs should be identified so that lifestyle modification can be started. The risk scores though present are available for individual diseases, and no composite risk score for major NCDs exists. Risk prediction is a national priority, and it is in national interest to develop a composite risk score. A Community Based Assessment Checklist for NCDs has been developed the Ministry of Health and Family Welfare, Government of India, under National Program for Control and Prevention of Cancer, Diabetes, Cardiovascular Diseases and Stroke (NPCDCS) but the risk score does not include all the risk predictors and is not yet validated at the population level. A risk score before being applied to the general population needs to be validated. The NCDs, also called the lifestyle diseases, share the common risk factors, and if they share the risk factors, an integrated or a composite risk score should be present to predict the disease risk. The proposed research, therefore, provides the development of composite tool to integrate the various individual risk scores for major NCDs. Globally, to the best of our knowledge it is first of its kind and will help in the successful implementation of NCD programs.
| Methods|| |
Two different population-based studies will be used in the analysis. The data from the WHO STEPS Survey conducted in Haryana will be used to develop the predictive score. This will be complemented with the STEPS Survey conducted in Punjab, whose information will be used to validate the risk score. NCD risk will be assessed on the available secondary data collected from Punjab and Haryana using a standard WHO STEPS questionnaire on the various parameters. The distribution of risk profile is available for both Punjab and Haryana in a statewide survey undertaken under the National Health Mission.,
[Figure 1] gives the methodological framework of the study. Once the NCD risk profile is assessed, existing individual risk scores will be reviewed. Draft tool for composite risk score will be prepared. Multivariate logistic regression model will be applied, and the regression coefficient will be used to assign each variable category a score. The variables will be retained if they will make a significant contribution according to the likelihood ratio test. The nonsignificant factors will not be included. The composite risk score will be composed as the sum of individual risk scores. Each potential risk factor or the risk predictors such as sex, age, family history of CVDs, and blood pressure will be assessed in bivariate models using logistic regression and NCDs as the dependent variable. Then, risk factors with a P value <0.10 in the bivariate analysis will be included in a multiple logistic regression model using stepwise backward elimination with a significance level of 5%. The risk predictors in the final model will each be assigned a weighted score by rounding up all regression coefficients in the final model.
The coefficients from the resulting model will form the risk score. Risk score will be derived from the receiver operating characteristic (ROC) curve. The tool will be further validated.
The developed risk score will be externally validated using the data obtained from Haryana.
For the evaluation of the risk score, the Area under the ROC curve (AUC), sensitivity, specificity, and positive and negative predictive values (PPV and NPV) will be calculated. The optimal cut point will be determined using the Youden index, a single statistic that captures the performance of a diagnostic test (i.e., sensitivity + specificity - 1).
For the validation against the standard individual risk score, an individual risk score which has been validated in the Indian population will be selected and its performance on the ROC curve against the developed composite risk score will be seen for the reason whether its response is better or worse than the developed risk score. The AUC, sensitivity, specificity, and PPV and NPV will be calculated for both the risk scores, and the performance will be compared.
The risk score developed will be tested in a diverse population. The feasibility and acceptability for using composite risk score will be assessed on the basis of schedule-based interviews and the variables used to develop the composite risk score. The score will be tested in a representative population in urban, rural, and slum populations in Chandigarh. The objective will be achieved by risk assessment done by three health workers from health centers in Chandigarh. The health workers will be trained to implement the risk assessment using the composite risk score in the population by the investigator. The score will be piloted on 300 individuals. The risk profile will then be assessed by the investigator. Furthermore, the investigator will reassess the pilot on all 300 individuals within 3 days of the piloting done by the health worker. Using Kappa statistics, the percentage agreement between the health worker and the investigator will be calculated.
The ethical approval has been obtained from the Ethics Committee of Post Graduate Institute of Medical Education and Research, Chandigarh, India. The secondary data to be used in the study will be used by the approval by the principal investigator of the project.
| Discussion|| |
Risk scores predict the risk of the disease efficiently allowing the analysis of a disease condition over a particular period. Composite risk scores can be useful indicators of future cognition. The NCDs also called the lifestyle diseases share the common risk factors, and if they share the risk factors, an integrated or a composite risk score should be present to predict the disease risk. Risk prediction is a national priority, and it is in national interest to develop a composite risk score. Prediction of the non communicable diseases based the risk factors will help in devising prevention strategies and health care delivery planning. The CBAC developed by the Ministry of Health and Family Welfare, Government of India under NPCDCS for early detection of NCDs highlight the need for early assessment to avert the major burden of NCDs. The proposed research, therefore provides the development and validation of the composite risk score for NCDs. The developed tool can be used for referring to the highest risk individuals to health care for further screening and may serve as an important part of prevention programs targeting chronic diseases.
Financial support and sponsorship
Conflicts of interest
There are no conflicts of interest.
| References|| |
World Health Organization. Global Status Report on Non Communicable Diseases 2014. Geneva: World Health Organization; 2014.
WHO. Global Status Report on Noncommunicable Diseases 2014. Geneva: World Health Organisation; 2014.
Harris MI. Undiagnosed NIDDM: Clinical and public health issues. Diabetes Care 1993;16:642-52.
Centers of Disease Control and Prevention. Strategies for reducing morbidity and mortality from diabetes through health-care system interventions and diabetes self-management education in community settings. A report on recommendations of the Task Force on Community Preventive Services. MMWR Recomm Rep 2001;50:1-5.
Wareham NJ, Griffin SJ. Should we screen for type 2 diabetes? Evaluation against National Screening Committee criteria. BMJ 2001;322:986-8.
Collins GS, Altman DG. Identifying patients with undetected gastro-oesophageal cancer in primary care: External validation of QCancer® (Gastro-Oesophageal). Eur J Cancer (Oxford, England 1990) 2013;49:1040-8.
Evans DG, Howell A. Breast cancer risk-assessment models. Breast Cancer Res 2007;9:213.
Gail MH, Brinton LA, Byar DP, Corle DK, Green SB, Schairer C, et al
. Projecting individualized probabilities of developing breast cancer for white females who are being examined annually. J Natl Cancer Inst 1989;81:1879-86.
Matsuno RK, Costantino JP, Ziegler RG, Anderson GL, Li H, Pee D, et al
. Projecting individualized absolute invasive breast cancer risk in Asian and Pacific Islander American women. J Natl Cancer Inst 2011;103:951-61.
Tice JA, Cummings SR, Smith-Bindman R, Ichikawa L, Barlow WE, Kerlikowske K. Using clinical factors and mammographic breast density to estimate breast cancer risk: Development and validation of a new predictive model. Ann Intern Med 2008;148:337-47.
Chen JY, Zhang AD, Lu HY, Guo J, Wang FF, Li ZC. CHADS2 versus CHA2DS2-VASc score in assessing the stroke and thromboembolism risk stratification in patients with atrial fibrillation: A systematic review and meta-analysis. J Geriatr Cardiol 2013;10:258-66.
D'Agostino RB, Wolf PA, Belanger AJ, Kannel WB. Stroke risk profile: Adjustment for antihypertensive medication. The Framingham study. Stroke 1994;25:40-3.
Kiyohara T, Kamouchi M, Kumai Y, Ninomiya T, Hata J, Yoshimura S, et al
. ABCD3 and ABCD3-I scores are superior to ABCD2 score in the prediction of short-and long-term risks of stroke after transient ischemic attack. Stroke 2014;45:418-25.
DeRight J, Jorgensen RS, Cabral MJ. Composite cardiovascular risk scores and neuropsychological functioning: A meta-analytic review. Ann Behav Med 2015;49:344-57.
Lloyd-Jones DM, Leip EP, Larson MG, D'Agostino RB, Beiser A, Wilson PW, et al
. Prediction of lifetime risk for cardiovascular disease by risk factor burden at 50 years of age. Circulation 2006;113:791-8.
Lloyd-Jones DM, Wilson PW, Larson MG, Beiser A, Leip EP, D'Agostino RB, et al
. Framingham risk score and prediction of lifetime risk for coronary heart disease. Am J Cardiol 2004;94:20-4.
Bernabe-Ortiz A, Smeeth L, Gilman RH, Sanchez-Abanto JR, Checkley W, Miranda JJ, et al
. Development and validation of a simple risk score for undiagnosed type 2 diabetes in a resource-constrained setting. J Diabetes Res 2016;2016:8790235.
Collins GS, Altman DG. External validation of QDSCORE(®) for predicting the 10-year risk of developing Type 2 diabetes. Diabet Med 2011;28:599-607.
Mohan V, Sandeep S, Deepa M, Gokulakrishnan K, Datta M, Deepa R. A diabetes risk score helps identify metabolic syndrome and cardiovascular risk in Indians-the Chennai Urban Rural Epidemiology Study (CURES-38). Diabetes Obes Metab 2007;9:337-43.
Schulze MB, Hoffmann K, Boeing H, Linseisen J, Rohrmann S, Möhlig M, et al
. An accurate risk score based on anthropometric, dietary, and lifestyle factors to predict the development of type 2 diabetes. Diabetes Care 2007;30:510-5.
Angulo P, Hui JM, Marchesini G, Bugianesi E, George J, Farrell GC, et al
. The NAFLD fibrosis score: A noninvasive system that identifies liver fibrosis in patients with NAFLD. Hepatology 2007;45:846-54.
Raszeja-Wyszomirska J, Szymanik B, Lawniczak M, Kajor M, Chwist A, Milkiewicz P, et al
. Validation of the BARD scoring system in Polish patients with nonalcoholic fatty liver disease (NAFLD). BMC Gastroenterol 2010;10:67.
Palekar NA, Naus R, Larson SP, Ward J, Harrison SA. Clinical model for distinguishing nonalcoholic steatohepatitis from simple steatosis in patients with nonalcoholic fatty liver disease. Liver Int 2006;26:151-6.
Haroon S, Adab P, Riley RD, Marshall T, Lancashire R, Jordan RE. Predicting risk of COPD in primary care: Development and validation of a clinical risk score. BMJ Open Respir Res 2015;2:e000060.
Steer J, Gibson J, Bourke SC. The DECAF Score: Predicting hospital mortality in exacerbations of chronic obstructive pulmonary disease. Thorax 2012;67:970-6.
Martinez FJ, Raczek AE, Seifer FD, Conoscenti CS, Curtice TG, D'Eletto T, et al
. Development and initial validation of a self-scored COPD Population Screener Questionnaire (COPD-PS). COPD 2008;5:85-95.
Operational Guidelines for Prevention, Screening and Control of Non-Communicable Diseases: Hypertension, Diabetes and Common Cancers (oral, breast and cervix). Ministry of Health and Family Welfare. Government of India. New Delhi 2016.
Thakur JS, Jeet G, Pal A, Singh S, Singh A, Deepti SS, et al
. Profile of risk factors for non-communicable diseases in Punjab, Northern India: Results of a state-wide STEPS survey. PLoS One 2016;11:e0157705.
Thakur JS, Jeet G, Nangia R, Singh D, Grover S, Lyngdoh T, et al
. Non-communicable diseases risk factors and their determinants: A cross-sectional state-wide STEPS survey, Haryana, North India. PLOS ONE. 2019;14.
Youden WJ. Index for rating diagnostic tests. Cancer 1950;3:32-5.