Skip to main content

Barriers and Recommendations for Developing a Data Commons for the Implementation and Application of Cardiovascular Disease and Diabetes Risk Scoring in the Philippines


Purpose of Review

Cardiovascular diseases (CVDs) and diabetes are the primary causes of death in the Philippines. This manuscript reviewed previous studies on the use of predictive analytics for CVD and diabetes risk scoring. This paper also discussed barriers and strategies on how to access/generate available data sets for CVDs and diabetes in the country.

Recent Findings

CVD and diabetes risk scoring requires the availability of data sets related to such diseases. Although the Philippines has taken strides to implement the Philippine National eHealth solution, such a program does not include strategies toward the use of predictive analytics for CVD and diabetes risk scoring.


CVD and diabetes risk scoring research is particularly limited in the Philippines due to challenges related to costs, gaps in policies, and stakeholder involvement. A possible theoretical framework for the analysis and utilization of data sets as well as recommendations and research directions were discussed in this manuscript.

This is a preview of subscription content, access via your institution.


Papers of particular interest, published recently, have been highlighted as: • Of importance •• Of major importance

  1. 1.

    Raghupathi W, Raghupathi V. Big data analytics in healthcare: promise and potential. Health Inf Sci Syst. 2014;2:3.

    Article  Google Scholar 

  2. 2.

    May 2016 MA-A on the 16. Update: GPs warned about error in cardiovascular risk calculator [Internet]. [cited 2019 Jul 21]. Available from:

  3. 3.

    Baena-Díez JM, Subirana I, Ramos R, Gómez de la Cámara A, Elosua R, Vila J, et al. Validity assessment of low-risk SCORE function and SCORE function calibrated to the Spanish population in the FRESCO cohorts. Rev Esp Cardiol. 2018;71:274–82.

    Article  Google Scholar 

  4. 4.

    •• Damen JA, Pajouheshnia R, Heus P, Moons KGM, Reitsma JB, Scholten RJPM, et al. Performance of the Framingham risk models and pooled cohort equations for predicting 10-year risk of cardiovascular disease: a systematic review and meta-analysis. BMC Med. 2019;17:109 A comprehensive search that identified 1585 studies, of which 38 were included, describing a total of 112 external validations with the results describing that, on average, all models overestimate the 10-year risk of coronary heart disease and CVD. Overestimation was most pronounced for high-risk individuals and European populations.

    Article  Google Scholar 

  5. 5.

    •• Eichler K, Milo A, Puhan JS. Accuracy of risk assessment in primary prevention of cardiovascular disease. 2019 [cited 2019 Jul 21]; Available from: A contributing letter to the editor pointing out that the systematic review by Brindle et al. is an important contribution showing the variable calibration of the Framingham score across different populations. However, it also lacks a rigorous evaluation of factors which may have influence on calibration. The authors believe that further work is needed to learn more about factors influencing predictive performance of the Framingham risk score across different populations, which can have direct implications for use in clinical practice.

  6. 6.

    Omech B, Mwita JC, Tshikuka J-G, Tsima B, Nkomazna O, Amone-Polak K. Validity of the Finnish diabetes risk score for detecting undiagnosed type 2 diabetes among general medical outpatients in Botswana [Internet]. J Diabetes Res. 2016; [cited 2019 Jul 21]. Available from:

  7. 7.

    Lee KL, Pryor DB, Harrell FE, Califf RM, Behar VS, Floyd WL, et al. Predicting outcome in coronary disease statistical models versus expert clinicians. Am J Med. 1986;80:553–60.

    CAS  Article  Google Scholar 

  8. 8.

    Jimeno C. A summary of the Philippines UNITE for diabetes clinical practice guidelines for the diagnosis and management of diabetes (part I: screening and diagnosis of DM). J ASEAN Fed Endocr Soc. 2014;26:26.

    Google Scholar 

  9. 9.

    Deaths in the Philippines, 2016 | Philippine Statistics Authority [Internet]. [cited 2018 Dec 20]. Available from:

  10. 10.

    Cardiovascular disease is still the country’s top killer | Inquirer Lifestyle [Internet]. [cited 2018 Dec 20]. Available from:

  11. 11.

    •• ABS-CBN News. Bilang ng mga may diabetes sa PH, posibleng umakyat sa 7.8 milyon sa 2030 [Internet]. ABS-CBN News. [cited 2019 May 20]. Available from: A news update estimating that by 2030, the Philippines will have about 7.8 million diabetics, ranking 9thin the list of countries with the highest number of diabetics according to the Philippine College of Physicians.

  12. 12.

    Cardiovascular Disease | Department of Health website [Internet]. [cited 2018 Dec 20]. Available from:

  13. 13.

    Twitter. Heart disease still leading cause of death in PH [Internet]. [cited 2018 Dec 20]. Available from:

  14. 14.

    Philippines embraces efforts to step up cardiovascular disease care [Internet]. [cited 2018 Dec 20]. Available from:

  15. 15.

    CALIMAG MMP. [Philippines] Health databases in the era of information technology: the Philippine scene. Jpn Med Assoc J. 2014;57:207–11.

    Google Scholar 

  16. 16.

    Philippine eHealth System [Internet]. [cited 2018 Dec 20]. Available from:

  17. 17.

    Dumlao-Abadilla D. 5.1% of Filipinos obese due to affluence, unhealthy diet [Internet]. [cited 2019 Jul 21]. Available from:

  18. 18.

    The relationship between obesity, diabetes and the heart [Internet]. Mount Elizabeth Hospital. [cited 2019 Jul 21]. Available from:

  19. 19.

    Heart disease, early complication of type 2 diabetes [Internet]. [cited 2019 Jul 21]. Available from:

  20. 20.

    Four key areas of need identified to address challenges of aging and CVD in APAC, says report [Internet]. Healthcare IT News. 2019 [cited 2019 Jul 21]. Available from:

  21. 21.

    Foraker RE, Greiner M, Sims M, Tucker KL, Towfighi A, Bidulescu A, et al. Comparison of risk scores for the prediction of stroke in African Americans: findings from the Jackson Heart Study. Am Heart J. 2016;177:25–32.

    Article  Google Scholar 

  22. 22.

    Hebert C, Shivade C, Foraker R, Wasserman J, Roth C, Mekhjian H, et al. Diagnosis-specific readmission risk prediction using electronic health data: a retrospective cohort study. BMC Med Inform Decis Mak. 2014;14:65.

    Article  Google Scholar 

  23. 23.

    • Goto S, Goto S, Pieper KS, Bassand J-P, Camm AJ, Fitzmaurice DA, et al. New AI prediction model using serial PT-INR measurements in AF patients on VKAs: GARFIELD-AF. Eur Heart J Cardiovasc Pharmacother [Internet]. 2019 [cited 2019 Dec 13]; Available from: A new AI model developed using a multilayer neural network for predicting clinical outcomes in AF patients up to 1 year based on sequential measures of prothrombin time-international normalized ratio (PT-INR) within 30 days of enrolment was found to perform better than time in therapeutic range (TTR).

  24. 24.

    •• Mansoor H, Jo A, Beau De Rochars VM, Pepine CJ, Mainous AG. Novel self-report tool for cardiovascular risk assessment. J Am Heart Assoc. 2019;8:e014123 Developed a simple, easy-to-use, and novel risk score to predict cardiovascular events in adults from self-reported information without need for laboratory or physical examination data. The information only included 6 factors including male sex, age, current smoking, diabetes mellitus, hypertension, and family history of premature myocardial infarction.

    Article  Google Scholar 

  25. 25.

    • Chen Y, Qi B. Representation learning in intraoperative vital signs for heart failure risk prediction. BMC Med Inform Decis Mak. 2019;19:260 Demonstrated that gradient boosting machine learning technique can be used as a classifier in the prediction of heart failure by statistical feature representation. Results of the study also showed that representation learning model of vital signs monitoring data of intraoperative patients can effectively capture the physiological characteristics of postoperative heart failure.

    Article  Google Scholar 

  26. 26.

    • Hwang I-C, Cho G-Y, Choi H-M, Yoon YE, Park JJ, Park J-B, et al. Derivation and validation of a mortality risk prediction model using global longitudinal strain in patients with acute heart failure. Eur Heart J Cardiovasc Imaging. 2019; This is the first study to develop a mortality risk prediction model for patients with AHF incorporating left ventricular-global longitudinal strain as the left ventricular function parameter, and other clinical factors using 3248 patients.

  27. 27.

    • Huang J, Xiang Y, Zhang H, Wu N, Chen X, Wu L, et al. Plasma level of interferon-γ predicts the prognosis in patients with new-onset atrial fibrillation. Heart Lung Circ. 2019; The first study to have shown that plasma levels of IFN-γ could provide incremental prognostic value supplementary to that obtained from the CHA2DS2-VASc scores for predicting of stroke and all-cause mortality.

  28. 28.

    • May HT, Lappé DL, Knowlton KU, Muhlestein JB, Anderson JL, Horne BD. Prediction of long-term incidence of chronic cardiovascular and cardiopulmonary diseases in primary care patients for population health monitoring: the Intermountain Chronic Disease Model (ICHRON). Mayo Clin Proc [Internet]. 2018 [cited 2019 May 19];0. Available from: An augmented intelligence clinical decision tool for primary care, ICHRON, was developed using common laboratory parameters, providing good discrimination of ChrD risk at 3 and 10 years. The sex-specific ICHRON was composed of comprehensive metabolic profile and complete blood count components and age using 144,254 patients.

  29. 29.

    Pencina MJ, D’Agostino RB, Larson MG, Massaro JM, Vasan RS. Predicting the thirty-year risk of cardiovascular disease: the Framingham Heart Study. Circulation. 2009;119:3078–84.

    Article  Google Scholar 

  30. 30.

    • Tran-Duy A, McDermott R, Knight J, Hua X, Barr ELM, Arabena K, et al. Development and use of prediction models for classification of cardiovascular risk of remote indigenous Australians. Heart Lung Circ. 2019; The first study to utilize a seven-factor risk score that satisfactorily stratified 5-year risk of CVD in an indigenous Australian cohort using 1583 individuals. The risk score consisted of sex, age, systolic blood pressure, diabetes mellitus, waist circumference, triglycerides, and albumin creatinine ratio.

  31. 31.

    •• Alaa AM, Bolton T, Di Angelantonio E, Rudd JHF, van der Schaar M. Cardiovascular disease risk prediction using automated machine learning: a prospective study of 423,604 UK Biobank participants. PLoS One. 2019;14:e0213653 The first study that utilized an AutoPrognosis model, an algorithmic tool that automatically selects and tunes ensembles of machine learning modeling pipelines (comprising data imputation, feature processing, classification and calibration algorithms), to improve the accuracy of CVD risk prediction in the UK Biobank population. The study also uncovered novel predictors for CVD disease that may now be tested in prospective studies.

    CAS  Article  Google Scholar 

  32. 32.

    • Tadic M, Cuspidi C, Celic V, Pencic B, Mancia G, Grassi G, et al. The prognostic effect of circadian blood pressure pattern on long-term cardiovascular outcome is independent of left ventricular remodeling. J Clin Med. 2019;8 The first study to have assessed the importance of reverse-dipping BP pattern as a predictor of adverse CV events independently of nighttime SBP and LV remodeling during long-term follow-up.

  33. 33.

    Güler B, Özler S, Kadıoğlu N, Özkan E, Güngören MS, Çelen Ş. Is the low AMH level associated with the risk of cardiovascular disease in obese pregnants? J Obstet Gynaecol. 2019:1–6.

  34. 34.

    • Anderson JL, Le VT, Min DB, Biswas S, Minder CM, McCubrey RO, et al. Comparison of three atherosclerotic cardiovascular disease risk scores with and without coronary calcium for predicting revascularization and major adverse coronary events in symptomatic patients undergoing positron emission tomography-stress testing. Am J Cardiol. 2019; The first study to have showed that risk scores including coronary artery calcium score and multiethnic study of atherosclerosis risk equation may be particularly useful in primary coronary risk assessment when considering whom to refer for positron emission tomography -stress testing.

  35. 35.

    •• Yu D, Shang J, Cai Y, Wang Z, Zhang X, Zhao B, et al. Derivation and external validation of a risk prediction algorithm to estimate future risk of cardiovascular death among patients with type 2 diabetes and incident diabetic nephropathy: prospective cohort study. BMJ Open Diabetes Res Care. 2019;7:e000735 Developed a novel risk score to estimate the future risk of cardiovascular death specifically for patients with type 2 diabetes and DN.

    Article  Google Scholar 

  36. 36.

    • Arellano-Campos O, Gómez-Velasco DV, Bello-Chavolla OY, Cruz-Bautista I, Melgarejo-Hernandez MA, Muñoz-Hernandez L, et al. Development and validation of a predictive model for incident type 2 diabetes in middle-aged Mexican adults: the metabolic syndrome cohort. BMC Endocr Disord. 2019;19:41 The constructed models can be implemented to predict diabetes risk and represent the largest prospective effort (n=7637) for the study metabolic diseases in Latin-American population.

    Article  Google Scholar 

  37. 37.

    Hippisley-Cox J, Coupland C, Robson J, Sheikh A, Brindle P. Predicting risk of type 2 diabetes in England and Wales: prospective derivation and validation of QDScore. BMJ. 2009;338:b880.

    Article  Google Scholar 

  38. 38.

    Chen L, Magliano DJ, Balkau B, Colagiuri S, Zimmet PZ, Tonkin AM, et al. AUSDRISK: an Australian type 2 diabetes risk assessment tool based on demographic, lifestyle and simple anthropometric measures. Med J Aust. 2010;192:197–202.

    Article  Google Scholar 

  39. 39.

    Jung SY, Lee SJ, Kim SH, Jung KM. A predictive model of health outcomes for young people with type 2 diabetes. Asian Nurs Res (Korean Soc Nurs Sci). 2015;9:73–80.

    Google Scholar 

  40. 40.

    Turi KN. Predicting risk of type 2 diabetes by using data on easy-to-measure risk factors. Prev Chronic Dis [Internet]. 2017 [cited 2019 May 20];14. Available from:

  41. 41.

    Lotfaliany M, Hadaegh F, Asgari S, Mansournia MA, Azizi F, Oldenburg B, et al. Non-invasive risk prediction models in identifying undiagnosed type 2 diabetes or predicting future incident cases in the Iranian population. Arch Iran Med. 2019;22:116–24.

    PubMed  Google Scholar 

  42. 42.

    • Davis WA, Hamilton EJ, Bruce DG, Davis TME. Development and validation of a simple hip fracture risk prediction tool for type 2 diabetes: the Fremantle Diabetes Study phase I. Diabetes Care. 2019;42:102–9 This study developed a novel Fremantle Diabetes Study Phase I hip fracture risk equation as a simple validated adjunct to type 2 diabetes management that uses variables that are readily available in routine care.

    Article  Google Scholar 

  43. 43.

    • Hu H, Wang J, Han X, Li Y, Miao X, Yuan J, et al. Prediction of 5-year risk of diabetes mellitus in relatively low risk middle-aged and elderly adults. Acta Diabetol [internet]. 2019 [cited 2019 Dec 12]; available from: A study that determined the potential risk factors and constructed a predictive model of diabetic risk among a relatively low risk middle-aged and elderly Chinese population.

    Article  Google Scholar 

  44. 44.

    •• Louangdouangsithidet S, Jiamjarasrangsi W, Sapwarobol S. A risk scores for predicting prevalence of diabetes in the LAO population. Int J Diabetes Dev Countries. 2019;39:154–9 The first study to have identified age, waist circumference, hypertension, and family history of diabetes could be utilized to identify Lao individuals at high risk of undiagnosed diabetes.

    Article  Google Scholar 

  45. 45.

    Zeng J, Chen S, Ye J, Chen Y, Lei L, Liu X, et al. A simple risk score model for predicting contrast-induced nephropathy after coronary angiography in patients with diabetes. Clin Exp Nephrol. 2019;23:969–81.

    CAS  Article  Google Scholar 

  46. 46.

    • Martinez-Millana A, Argente-Pla M, Valdivieso Martinez B, Traver Salcedo V, Merino-Torres JF. Driving type 2 diabetes risk scores into clinical practice: performance analysis in hospital settings. J Clin Med. 2019;8:107 A study that showed that despite their potential use, electronic health records should be carefully analyzed before the massive use of Type 2 Diabetes risk scores for the identification of high-risk subjects, and subsequent targeting of preventive actions.

    Article  Google Scholar 

  47. 47•.

    . Yusufi FNK, Ahmed A, Ahmad J. Modelling and developing diabetic retinopathy risk scores on Indian type 2 diabetes patients. Int J Diabetes Dev Ctries. 2019;39:29–38 The first study to have developed DR risk scores and compute prevalence and incidence probabilities of DR in Indian type 2 diabetes mellitus patients.

    CAS  Article  Google Scholar 

  48. 48.

    •• Jacobsen LM, Larsson HE, Tamura RN, Vehik K, Clasen J, Sosenko J, et al. Predicting progression to type 1 diabetes from ages 3 to 6 in islet autoantibody positive TEDDY children. Pediatr Diabetes. 2019;20:263–70 This study showed the application of precision medicine techniques to predict progression to diabetes over a 3-year window in TEDDY children (n=363). Logistic regression modeling identified 5 significant predictors - IA-2A status, hemoglobin A1c, body mass index Z-score, single-nucleotide polymorphism rs12708716_G, and a combination marker of autoantibody number plus fasting insulin level.

    CAS  Article  Google Scholar 

  49. 49.

    • Sulaiman N, Mahmoud I, Hussein A, Elbadawi S, Abusnana S, Zimmet P, et al. Diabetes risk score in the United Arab Emirates: a screening tool for the early detection of type 2 diabetes mellitusBMJ Open Diabetes Res Care [Internet]. 2018 [cited 2019 Dec 12];6. Available from: This study showed the development of a simple, non-invasive risk score model to help to identify those at high risk of having diabetes among UAE citizens.

  50. 50.

    •• Nagata M, Takai K, Yasuda K, Heracleous P, Yoneyama A. Prediction models for risk of type-2 diabetes using health claims. Proceedings of the BioNLP 2018 workshop. 2018. p. 172–176. The first study that showed that the XGBoost model with health claim variables achieved a higher performance compared to the LSTM and L1-regularized logistic regression models.

  51. 51.

    Damen JAAG, Hooft L, Schuit E, Debray TPA, Collins GS, Tzoulaki I, et al. Prediction models for cardiovascular disease risk in the general population: systematic review. BMJ. 2016;353:i2416.

    Article  Google Scholar 

  52. 52.

    Moons KGM, Kengne AP, Woodward M, Royston P, Vergouwe Y, Altman DG, et al. Risk prediction models: I. Development, internal validation, and assessing the incremental value of a new (bio)marker. Heart. 2012;98:683–90.

    Article  Google Scholar 

  53. 53.

    Bosomworth NJ. Practical use of the Framingham risk score in primary prevention: Canadian perspective. Can Fam Physician. 2011;57:417–23.

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    Preiss D, Kristensen SL. The new pooled cohort equations risk calculator. Can J Cardiol. 2015;31:613–9.

    Article  Google Scholar 

  55. 55.

    A comprehensive review of predictive risk models for cardiovascular disease - American College of Cardiology [Internet]. [cited 2019 May 19]. Available from:

  56. 56.

    Hu FB. Globalization of diabetes: the role of diet, lifestyle, and genes. Diabetes Care. 2011;34:1249–57.

    Article  Google Scholar 

  57. 57.

    Ohman EM, Bhatt DL, Steg PG, Goto S, Hirsch AT, Liau C-S, et al. The REduction of Atherothrombosis for Continued Health (REACH) Registry: an international, prospective, observational investigation in subjects at risk for atherothrombotic events-study design. Am Heart J. 2006;151:786.e1–10.

    Article  Google Scholar 

  58. 58.

    • Wan EYF, Fong DYT, Fung CSC, Yu EYT, Chin WY, Chan AKC, et al. Development of a cardiovascular diseases risk prediction model and tools for Chinese patients with type 2 diabetes mellitus: a population-based retrospective cohort study. Diabetes Obes Metab. 2018;20:309–18 This is a population based retrospective cohort study that developed and validated a CVD risk prediction model for Chinese T2DM patients.

    CAS  Article  Google Scholar 

  59. 59.

    Yatsuya H, Iso H, Li Y, Yamagishi K, Kokubo Y, Saito I, et al. Development of a risk equation for the incidence of coronary artery disease and ischemic stroke for middle-aged Japanese - Japan public health center-based prospective study. Circ J. 2016;80:1386–95.

    Article  Google Scholar 

  60. 60.

    Penaranda B. Frailty modeling of cardiovascular risk factors among Filipino. [Internet]. University of the Philippines Manila; 2011 [cited 2019 Dec 2]. Available from:

  61. 61.

    Carandang EL. Online CHD risk assessment calculator based on Philippine Heart Association guidelines and dataset [Internet] [Thesis]. 2016 [cited 2019 Oct 26]. Available from:

  62. 62.

    Framingham Heart Study [Internet]. [cited 2019 Dec 2]. Available from:

  63. 63.

    Framingham Heart Study (FHS) | National Heart, Lung, and Blood Institute (NHLBI) [Internet]. [cited 2019 Dec 2]. Available from:

  64. 64.

    Department of Health, Department of Science and Technology. Philippine eHealth Strategic Framework and Plan (2014–2020). 2014 Apr.

  65. 65.

    House panel approves proposed National eHealth System and Services Act [Internet]. Manila Bulletin News. [cited 2020 Jan 31]. Available from:

  66. 66.

    Salisi J, Cruz JP, Lu SF, Fernandez-Marcelo P. The Philippine policy context for eHealth. Acta Medica Philippina. 2016;50:206–14.

    Google Scholar 

  67. 67.

    •• Sharma A, Harrington RA, McClellan MB, Turakhia MP, Eapen ZJ, Steinhubl S, et al. Using digital health technology to better generate evidence and deliver evidence-based care. J Am Coll Cardiol. 2018;71:2680–90 A review manuscript that delineated a framework for appropriately using digital health technologies in healthcare delivery and research.

    Article  Google Scholar 

  68. 68.

    UVMHN implements first phase of a unified electronic health record system [Internet]. Vermont Business Magazine. 2019 [cited 2020 Jan 31]. Available from:

  69. 69.

    Successful implementation of electronic medical record system in Brooklyn [Internet]. [cited 2020 Jan 31]. Available from:

  70. 70.

    Wilkie R. VA moving to deliver electronic health record modernization [Internet]. Military Times. 2019 [cited 2020 Jan 31]. Available from:

  71. 71.

    MIDUS - Midlife in the United States, a national longitudinal study of health and well-being [Internet]. [cited 2019 Dec 12]. Available from:

  72. 72.

    Studies | The Dunedin Study - Dunedin multidisciplinary health & development research unit [Internet]. [cited 2019 Dec 2]. Available from:

  73. 73.

    Soria MLB, Sy R, Vega BS, Ty-Willing T, Salunat-Flores J, Velandria FV, et al. Philippine cardiovascular outcome study-diabetes mellitus (PHILCOS-DM): a cohort study of the eight-year incidence of diabetes mellitus in NCR, Region 3 and Region 4. Philipp J Intern Med. 45:211–7.

  74. 74.

    Velandria FV, Soria MLB, Ty-Willing BS. The eight-year incidence of diabetes mellitus and other cardiovascular outcomes of the 1998 FNRI-NNS, in the NCR, Region 3 and Region 4. Philipp J Cardiol. 35:10–21.

  75. 75.

    Open Data Philippines [Internet]. [cited 2019 May 27]. Available from:

  76. 76.

    Philippine Statistics Authority. OpenSTAT [Internet]. [cited 2019 Dec 2]. Available from:

  77. 77.

    Department of Health Philippines. Unified Health Management Information System [Internet]. [cited 2019 Dec 2]. Available from:

  78. 78.

    About UnEMR [Internet]. UnEMR. [cited 2019 Oct 26]. Available from:

  79. 79.

    REDCap [Internet]. [cited 2019 Oct 26]. Available from:

  80. 80.

    Republic Act 10173 – Data Privacy Act of 2012 [Internet]. National Privacy Commission. 2016 [cited 2019 Dec 2]. Available from:

  81. 81.

    Philippine National Health Research System and the Philippine National Health Research Ethics Board [Internet]. [cited 2019 Dec 2]. Available from:

  82. 82.

    •• Marcelo A, Medeiros D, Ramesh K. Transforming health systems through good digital health governance [Internet]. Asian Development Bank; 2018 [cited 2019 Dec 14]. Available from: This paper highlights efforts to develop a health Information and communication technology governance architecture framework through consultations and close collaboration with experts.

  83. 83.

    Good Governance Guide | VLGA | Victorian Local Governance Association [Internet]. [cited 2019 Dec 14]. Available from:

Download references


Dr. Gerard G. Dumancas would like to acknowledge the University of San Agustin Center for Informatics and the Office of Research and Global Relations for hosting his engagement as a Balik Scientist.


This work is financially supported by the Department of Science and Technology - Balik Scientist Program of the Philippine Council for Health Research and Development.

Author information



Corresponding author

Correspondence to Gerard G. Dumancas.

Ethics declarations

Conflict of Interest

The authors declare that they have no conflict of interest.

Human and Animal Rights and Informed Consent

This article does not contain any studies with human or animal subjects performed by any of the authors.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

This article is part of the Topical Collection on Cardiovascular Disease

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Dumancas, G.G., de Castro, R., Saludes, J.P. et al. Barriers and Recommendations for Developing a Data Commons for the Implementation and Application of Cardiovascular Disease and Diabetes Risk Scoring in the Philippines. Curr Epidemiol Rep 7, 77–88 (2020).

Download citation


  • Cardiovascular disease
  • Diabetes
  • Philippine health
  • Predictive modeling
  • Disease risk scoring
  • Electronic health records