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A mathematical framework of SMS reminder campaigns for pre- and post-diagnosis check-ups using socio-demographics: An in-silco investigation into breast cancer

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  • Savchenko, Elizaveta
  • Rosenfeld, Ariel
  • Bunimovich-Mendrazitsky, Svetlana

Abstract

Timely pre- and post-diagnosis check-ups are critical for various diseases, in general, and for cancer , in particular, as these often lead to better outcomes. Several socio-demographic properties have been identified as strongly connected with both clinical dynamics and (indirectly) with different individual check-up behaviors. Unfortunately, existing check-up policies typically consider only the former association explicitly. In this work, we propose a novel computational framework, accompanied by a high-resolution computer simulation, to investigate and optimize socio-demographic-based Short Messaging Service (SMS) reminder campaigns for check-ups. We demonstrate our computational framework using extensive real-world data from the United States (US) population, focusing on breast cancer. Our results indicate that optimizing an SMS reminder campaign based solely on simple socio-demographic features can bring about a statistically significant reduction in mortality rate compared to alternative campaigns. These results indicate SMS reminder campaigns for pre- and post-diagnosis check-ups can be instrumental in improving healthcare outcomes. However, additional research is needed to bring about applicative tools.

Suggested Citation

  • Savchenko, Elizaveta & Rosenfeld, Ariel & Bunimovich-Mendrazitsky, Svetlana, 2024. "A mathematical framework of SMS reminder campaigns for pre- and post-diagnosis check-ups using socio-demographics: An in-silco investigation into breast cancer," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).
  • Handle: RePEc:eee:soceps:v:95:y:2024:i:c:s0038012124002465
    DOI: 10.1016/j.seps.2024.102047
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    References listed on IDEAS

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    1. Ramy Elitzur & Dmitry Krass & Eyal Zimlichman, 2023. "Machine learning for optimal test admission in the presence of resource constraints," Health Care Management Science, Springer, vol. 26(2), pages 279-300, June.
    2. Facundo Piguillem & Liyan Shi, 2022. "Optimal Covid-19 Quarantine and Testing Policies," The Economic Journal, Royal Economic Society, vol. 132(647), pages 2534-2562.
    3. J. Dunstan & F. Villena & J.P. Hoyos & V. Riquelme & M. Royer & H. Ramírez & J. Peypouquet, 2023. "Predicting no-show appointments in a pediatric hospital in Chile using machine learning," Health Care Management Science, Springer, vol. 26(2), pages 313-329, June.
    4. Billah, Baki & King, Maxwell L. & Snyder, Ralph D. & Koehler, Anne B., 2006. "Exponential smoothing model selection for forecasting," International Journal of Forecasting, Elsevier, vol. 22(2), pages 239-247.
    5. Enrico Zio, 2013. "Monte Carlo Simulation: The Method," Springer Series in Reliability Engineering, in: The Monte Carlo Simulation Method for System Reliability and Risk Analysis, edition 127, chapter 0, pages 19-58, Springer.
    6. Amit Yaniv-Rosenfeld & Elizaveta Savchenko & Ariel Rosenfeld & Teddy Lazebnik, 2023. "Scheduling BCG and IL-2 Injections for Bladder Cancer Immunotherapy Treatment," Mathematics, MDPI, vol. 11(5), pages 1-13, February.
    7. Teddy Lazebnik & Labib Shami & Svetlana Bunimovich-Mendrazitsky, 2022. "Spatio-Temporal influence of Non-Pharmaceutical interventions policies on pandemic dynamics and the economy: the case of COVID-19," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 35(1), pages 1833-1861, December.
    8. Isabelle Rao & Adir Shaham & Amir Yavneh & Dor Kahana & Itai Ashlagi & Margaret L. Brandeau & Dan Yamin, 2020. "Predicting and improving patient-level antibiotic adherence," Health Care Management Science, Springer, vol. 23(4), pages 507-519, December.
    9. Shami, Labib & Lazebnik, Teddy, 2023. "Financing and managing epidemiological-economic crises: Are we ready for another outbreak?," Journal of Policy Modeling, Elsevier, vol. 45(1), pages 74-89.
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