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01-02-2020 | Original Paper

Minimizing Health-Compromising Behaviors via School-Based Programs: An Optimization Approach

Authors: Niloofar Bavarian, Banafsheh Behzad, Sheena Cruz

Published in: Journal of Prevention | Issue 1/2020

Abstract

School health programs are united by their desire to promote health and health-related outcomes among youth. They are also united by the fact that their expected effects are contingent on successful program implementation, which is often impeded by a multitude of real-world barriers. Techniques used in management science may help optimize school-based programs by accounting for implementation barriers. In this exploratory study, we present a detailed example of the first known application of linear programming (LP), which is an optimization technique, to Positive Action (PA). PA is a social emotional and character development program that includes a six-unit, teacher-delivered, classroom curriculum. We specify how we used LP to calculate the optimal levels of program implementation needed to minimize substance use, subject to known levels of implementation barriers (e.g., disruptive behavior, teacher education, teacher attitudes towards character development, school resources, and school safety). We found that LP is a technique that can be applied to data from a school health program. Specifically, we were able to develop a model that calculated the number of lessons that should be taught to minimize a specific health-compromising behavior, given expected levels of predetermined implementation barriers. Our findings from this exploratory study support the utility of applying LP during the program planning and implementation processes of school health programs.

Simple linear regression equations used to form the objective function (Eq. 1):

$$SU = - 0.123u_{1} + 1.141$$

$$SU = - 0.128u_{2} + 1.145$$

$$SU = - 0.138u_{3} + 1.175$$

$$SU = - 0.152u_{4} + 1.214$$

$$SU = - 0.152u_{5} + 1.214$$

$$SU = - 0.152u_{6} + 1.214$$

Constrains related to student disruptive behavior: $u_{1} \le 3.356$² (obtained from nonequality ¹u₁ ≤ 0.7672 × 2.88 + 1.1462 = 3.356)

$$u_{2} \le 3.197$$

$$u_{3} \le 3.197$$

$$u_{4} \le 3.213$$

$$u_{5} \le 3.182$$

$$u_{6} \le 3.112$$

Constraints related to teacher’s various levels of education

$$u_{1} \ge 3.192$$

$$u_{2} \ge 3.070$$

$$u_{3} \ge 3.070$$

$$u_{6} \ge 2.966$$

Constraints related to teacher’s perceived responsibility to teach SECD

$$u_{1} \ge 3.342$$

$$u_{2} \ge 3.083$$

$$u_{3} \ge 3.083$$

Constraint related to school’s adequate resources $u_{1} \ge 3.218$

Constraints related to school safety

$$u_{1} \ge 2.713$$

$$u_{2} \ge 2.356$$

$$u_{3} \ge 2.356$$

$$u_{4} \ge 2.360$$

$$u_{5} \ge 2.253$$

$$u_{6} \ge 2.188$$

Constraints related to the range of the responses for each decision variable:

$$u_{1} , \ldots ,u_{6} \ge 1$$

$$u_{1} , \ldots ,u_{6} \le 5$$

Beets, M. W., Flay, B. R., Vuchinich, S., Acock, A., Li, K. K., & Allred, C. G. (2008). School climate and teachers’ beliefs and attitudes associated with implementation of the Positive Action program: A diffusion of innovations model. Prevention Science,9(4), 264–275. https://doi.org/10.1007/s11121-008-0100-2.CrossRefPubMed

Botvin, G. J. (2004). Advancing prevention science and practice: Challenges, critical issues, and future directions. Prevention Science,5(1), 69–72. https://doi.org/10.1023/B:PREV.0000013984.83251.8b.CrossRefPubMed

Centers for Disease Control and Prevention. (2016). Youth risk behavior surveillance system. https://www.cdc.gov/healthyYouth/data/yrbs/index.htm. Updated 11 August 2016. Accessed 12 July 2017.

Collins, L. M., Kugler, K. C., & Gwadz, M. V. (2015). Optimization of multicomponent behavioral and biobehavioral interventions for the prevention and treatment of HIV/AIDS. AIDSand Behavior,Supp 1, 197–214. https://doi.org/10.1007/s10461-015-1145-4.CrossRef

Cote-Lussier, C., & Fitzpatrick, C. (2016). Feelings of safety at school, socioemotional functioning, and classroom engagement. Journal of Adolescent Health,58(5), 543–550. https://doi.org/10.1016/j.jadohealth.2016.01.003.CrossRefPubMed

Dane, A. V., & Schneider, B. H. (1998). Program integrity in primary and early secondary prevention: Are implementation effects out of control? Clinical Psychology Review,18(1), 23–45. https://doi.org/10.1016/S0272-7358(97)00043-3.CrossRefPubMed

Domitrovich, C. E., Gest, S. D., Gill, S., Jones, D., & DeRousie, R. S. (2009). Individual factors associated with professional development training outcomes of the Head Start REDI Program. Early Education and Development,20(3), 402–430. https://doi.org/10.1080/10409280802680854.CrossRefPubMedPubMedCentral

Durlak, J. A., & DuPre, E. P. (2008). Implementation matters: A review of research on the influence of implementation on program outcomes and the factors affecting implementation. American Journal of Community Psychology,41(3–4), 327–350. https://doi.org/10.1007/s10464-008-9165-0.CrossRefPubMed

Dusenbury, L., Brannigan, R., Falco, M., & Hansen, W. B. (2003). A review of research on fidelity of implementation: Implications for drug abuse prevention in school settings. Health Education Research,18(2), 237–256. https://doi.org/10.1093/her/18.2.237.CrossRefPubMed

Earnshaw, S. R., Hicks, K., Richter, A., & Honeycutt, A. (2007). A linear programming model for allocating HIV prevention funds with state agencies: A pilot study. Health Care Management Science,10(3), 239–252. https://doi.org/10.1007/s10729-007-9017-8.CrossRefPubMed

Flessa, S. (2000). Where efficiency saves lives: A linear programme for the optimal allocation of health care resources in developing countries. Health Care Management Science,3(3), 249–267. https://doi.org/10.1023/A:1019053710258.CrossRefPubMed

Fuentes, M. C., Garcia, F., Gracia, E., & Lila, M. (2011). Self-concept and drug use in adolescence. Adicciones,23(3), 237.CrossRef

Hillier, F. S., & Lieberman, G. J. (2005). Introduction to operations research. New York: McGraw Hill.

Kuo, P. C., Schroeder, R. A., Mahaffey, S., & Bollinger, R. R. (2003). Optimization of operating room allocation using linear programming techniques. Journal of the American College of Surgeons,197(6), 889–895. https://doi.org/10.1016/j.jamcollsurg.2003.07.006.CrossRefPubMed

Lewis, K. M., Bavarian, N., Snyder, F. J., Acock, A., Day, J., Dubois, D. L., et al. (2012). Direct and mediated effects of a social-emotional and character development program on adolescent substance use. The International Journal of Emotional Education,4(1), 56.PubMedPubMedCentral

Linnell, J. D., Smith, M. H., Briggs, M., Brian, K. M., Scherr, R. E., Dharmar, M., et al. (2016). Evaluating the relationships among teacher characteristics, implementation factors, and student outcomes of children participating in an experiential school-based nutrition program. Pedagogy in Health Promotion,2(4), 256–265. https://doi.org/10.1177/2373379916649603.CrossRef

Ludwig, K. B., & Pittman, J. F. (1999). Adolescent prosocial values and self-efficacy in relation to delinquency, risky sexual behavior, and drug use. Youth and Society,30(4), 461–482. https://doi.org/10.1177/0044118X99030004004.CrossRef

Malloy, M., Acock, A., DuBois, D. L., Vuchinich, S., Silverthorn, N., Ji, P., et al. (2015). Teachers’ perceptions of school organizational climate as predictors of dosage and quality of implementation of a social-emotional and character development program. Prevention Science,16(8), 1086–1095. https://doi.org/10.1007/s11121-014-0534-7.CrossRefPubMed

Massachusetts Institute of Technology. (1977). Mathematical programming: an overview. Retrieved December 17, 2019 from http://web.mit.edu/15.053/www/AppliedMathematicalProgramming.pdf.

Mulholland, M. W., Abrahamse, P., & Bahl, V. (2005). Linear programming to optimize performance in a Department of Surgery. Journal of the American College of Surgeons,200(6), 861–868. https://doi.org/10.1016/j.jamcollsurg.2005.01.001.CrossRefPubMed

Pas, E. T., Waasdorp, T. E., & Bradshaw, C. P. (2015). Examining contextual influences on classroom-based implementation of positive behavior support strategies: Findings from a randomized controlled effectiveness trial. Prevention Science,16(8), 1096–1106. https://doi.org/10.1007/s11121-014-0492-0.CrossRefPubMed

Payne, A. A., Gottfredson, D. C., & Gottfredson, G. D. (2006). School predictors of the intensity of implementation of school-based prevention programs: Results from a national study. Prevention Science,7(2), 225–237. https://doi.org/10.1007/s11121-006-0029-2.CrossRefPubMed

Silver, E. A., Pyke, D. F., & Peterson, R. (1998). Inventory management and production planning and scheduling. New York: Wiley.

Tianviwat, S., Chongsuvivatwong, V., & Birch, S. (2009). Optimizing the mix of basic dental services for Southern Thai schoolchildren based on resource consumption, service needs, and parental preference. Community Dentistry and Oral Epidemiology,37(4), 372–380. https://doi.org/10.1111/j.1600-0528.2009.00481.x.CrossRefPubMed

Wang, B., Stanton, B., Deveaux, L., Lunn, S., Rolle, G., Adderley, R., et al. (2017). Multi-year school-based implementation and student outcomes of an evidence-based risk reduction intervention. Implementation Science,12(1), 16. https://doi.org/10.1186/s13012-016-0539-7.CrossRefPubMed

Title: Minimizing Health-Compromising Behaviors via School-Based Programs: An Optimization Approach
Authors: Niloofar Bavarian
Banafsheh Behzad
Sheena Cruz
Publication date: 01-02-2020
Publisher: Springer US
Published in: Journal of Prevention / Issue 1/2020
Print ISSN: 2731-5533
Electronic ISSN: 2731-5541
DOI: https://doi.org/10.1007/s10935-020-00577-1

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