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2017, Number 1

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Rev Cubana Estomatol 2017; 54 (1)

Dental caries risk index

González FV, Alegret RM, Martínez AJ, González FY

Language: Spanish
References: 26
Page: 34-47
PDF size: 238.55 Kb.


ABSTRACT

Introduction: Dental caries risk indices make it possible to focus on preventive actions and optimize health resources, leading to better results in dental care.
Objective: Evaluate the discriminating capacity of a dental caries risk index.
Methods: The study was conducted in two stages, corresponding to development and validation of the index. These took place during school years 2012-2013 and 2013-2014, respectively. The first stage was a case-control study with children aged 6-12 from three elementary schools in Santa Clara, Cuba. At the outset, the presence of caries was discarded and information was collected about a number of variables. Ten months later the 120 children with detected caries were selected as cases, and 240 controls were randomly chosen from among the remaining children. An index was developed applying a procedure based on Cramér's V and a caries prediction model based on logistic regression. The second stage was a cross-sectional study with 360 children to validate the index and contrast it with the prediction model on the basis of the area under the curve, recipient's operative characteristics, and other measures estimated with 2 × 2 tables.
Results: The index showed sensitivity, specificity and validity values of 87.5 %, 82.5 % y 84.2 % respectively. The regression model obtained percentage values of 80.8 %, 81.3 % and 81.1 %. The area under the curve was 0.889 for the former and 0.870 for the latter.
Conclusions: Results attest to the validity of the index obtained through Cramér's V values as an important tool to identify the risk for, and therefore the prevention and control of dental caries in children aged 6-12 resident in Santa Clara.


REFERENCES

  1. Pfeiffer RM. Extensions of criteria for evaluating risk prediction models for public health applications. Biostatistics. 2013;14(2):366-81.

  2. Oh SM, Stefani KM, Kim HC. Development and Application of Chronic Disease Risk Prediction Models. Yonsei Med J. 2014 July;55(4):853-60.

  3. Mures Quintana MJ, García Gallego A, Vallejo Pascual ME. Aplicación del análisis discriminante y regresión logística en el estudio de la morosidad en las entidades financieras. Comparación de resultados. Pecvnia. 2005;1(1):175-99.

  4. Singh K. Data analysis. In: Singh K. Quantitative social research methods. New Delhi: Chaman Enterprises; 2007. p. 122-77.

  5. Pérez Oropesa AL. Modelo de predicción de riesgo de caries dental en niños de la escuela "Antonio Maceo" [Tesis]. Villa Clara: Universidad Médica de Villa Clara; 2011.

  6. Collins G, Groot JA, Dutton S, Omar O, Shanyinde M, Tajar M, et al. External validation of multivariable prediction models: a systematic review of methodological conduct and reporting. BMC Medical Research Methodology. 2014;14(40):1-11.

  7. Collins GS, Reitsma JB, Altman DG, Moons KGM. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): The TRIPOD statement. Annals of Internal Medicine. 2015;162(1):55-64

  8. Sreenivasan PK, Prasad KVV, Javal SB. Oral health practices and prevalence of dental plaque and gingivitis among Indian adults. Clinical and Experimental Dental Research. 2016;2(1):6-17.

  9. Jeong E-G, Lee C-J, Lee K-H. The Clinical Test for Gingival Subsiding Effect by Use of Electric Gingival Stimulator. Int J Clin Prev Dent. 2016;12(3):163-8.

  10. Pinto T, Freitas G, Dutra D, Kantorski K, Moreira H. Frequency of mechanical removal of plaque as it relates to gingival inflammation. Oral Clinical Periodontology. 2013 [citado 2 Oct 2015];40(10):948-54. Disponible en: http://onlinelibrary.wiley.com/doi/10.1111/jcpe.12135/full

  11. Healthy mouths. Tooth savers Brushing Game [En línea]. 2016 [citado 4 oct 2016]. Disponible en: http://2min2x.org/index.html

  12. Grupo asesor metodológico estudios de salud de la familia. Manual para la intervención en la salud familiar. La Habana: Editorial Ciencias Médicas; 2002.

  13. Espinosa Fernandez R, Valencia Hitte R, Ceja Andrade I. Fluorosis dental. Lima: Ripano; 2012.

  14. González Ferrer V, Alegret Rodríguez M, González Ferrer Y, Moreno Arias A, Ramírez Marino M. Validación interna de modelo predictivo creado mediante nueva metodología aplicable en la atención primaria de salud. Medicent Electrón [En línea]. 2015 Oct-Dic [citado 2 Oct 2015];19(4):[aprox. 5 p.]. Disponible en: http://www.medicentro.sld.cu/index.php/medicentro/article/view/1966

  15. Rodríguez-Escudero JP, López-Jiménez F, Trejo-Gutiérrez Jorge F. Cardiología "basada en la evidencia": aplicaciones prácticas de la epidemiología. IV. Modelos de predicción de riesgo cardiovascular. Arch Cardiol Méx. [En línea]. 2012 Mar [citado 10 Ago 2014];82(1):[aprox. 3 p.]. Disponible en: http://www.scielo.org.mx/scielo.php?script=sci_arttext&pid=S1405-99402012000100011&lng=es

  16. Chaurasia A, Harel O. Using AIC in Multiple Linear Regression framework with Multiply Imputed Data. Health Serv Outcomes Res Methodol. 2012;12(2-3):219-33.

  17. Steyerberg EW, Vergouwe Y. Towards better clinical prediction models: seven steps for development and an ABCD for validation. European Heart Journal. 2014;35:1925-31.

  18. Healey JF. Bivariate association for nominal and ordinal level variables. In: Healey JF. Healey JF. The essentials of statistics: A tool for social research. 4th ed. Boston: Cengage Learning; 2015. p. 292-9.

  19. Ioannidis JP, Greenland S, Hlatky MA, Khoury MJ, Macleod MR, Moher D, et al. Increasing value and reducing waste in researchdesign, conduct, and analysis. Lancet. 2014;383:166-75.

  20. Ioannidis JPA. Scientific inbreeding and same-team replication: type D personality as an example. J Psychosom Res. 2012;73:408-10.

  21. Moons KGM, Pascal Kengne A, 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.

  22. Sanchez-Perez L, Golubov J, Irigoyen-Camacho ME, Moctezuma PA, Acosta-Gio E. Clinical, salivary, and bacterial markers for caries risk assessment in schoolchildren: a 4-year follow-up. Int J Paediatr Dent. 2009;19(3):186-92.

  23. Gamboa LF, Cortés A. Valoración del riesgo de caries: ¿mito o realidad? Univ Odontol. 2013;32(68) 69-79.

  24. Fontana M, Santiago E, Eckert GJ, Ferreira-Zandona AG. Risk factors of caries progression in a Hispanic school-aged population. J Dent Res. 2011;90(10):1189-96.

  25. Kleinbaum DG, Klein M. Assessing discriminatory performance of a binary logistic model: ROC curves. In: Kleinbaum DG, Klein M. Logistic Regression, Statistics for Biology and Health. New York: Springer Science + Business Media, LLC; 2010. p. 345-87.

  26. Duque de Estrada Riverón J. Modelo Predictivo para determinar el Riesgo de Caries Dental en niños de 6 a 12 años. Ciudad de Matanzas 2004-2006 [tesis]. Matanzas: Instituto Superior de Ciencias Médicas de La Habana; 2010 [citado 10 Ago 2014]. Disponible en: http://tesis.repo.sld.cu/291/




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