Universal Journal of Public Health Vol. 14(5), pp. 900 - 914
DOI: 10.13189/ujph.2026.140507
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A System Dynamics Predictive Model for Diabetic Polyneuropathy Control Integrating Clinical and Socioeconomic Determinants

Susanti Dwi Ariani ^1,2,*, Surya Hajar Fitria Dana ³, Ali Napiah Nasution ¹, Dodik Tugasworo ⁴, Syaiful Ramadhan Harahap ^5
1 Doctor of Medicine, Faculty of Medicine, Universitas Prima Indonesia, Medan, Indonesia
² Aulia Hospital, Pekanbaru, Indonesia
³ Faculty of Medicine, Universitas Riau, Pekanbaru, Indonesia
⁴ Faculty of Medicine, Universitas Diponegoro, Semarang, Indonesia
⁵ Department of Research and Community Service, Universitas Islam Indragiri, Tembilahan, Indonesia

ABSTRACT

Diabetic polyneuropathy (DPN) is a serious complication of diabetes mellitus (DM) that significantly affects the quality of life and imposes an additional burden on healthcare. The identification of predictors and formulation of predictive models are crucial steps for the successful prevention of DPN, especially in developing countries. A mixed-methods design combining a cross-sectional study and system dynamics modeling was used. Data from 54 respondents (27 cases and 27 controls) were analyzed using univariate, bivariate, multivariate, and logistic regression analyses to identify the significant determinants of DPN. A dynamic model using causal loop (CLD) and stock–flow (SFD) diagrams simulated disease progression and intervention scenarios over 36 months. Eight determinants were significantly associated with DPN: age (OR = 4.18), sex (OR = 2.37), knowledge (OR = 2.06), dietary habits (OR = 2.59), physical activity (OR = 1.97), medication adherence (OR = 1.88), lifestyle (OR = 2.05), and quality of life (OR = 1.78). Baseline simulation showed substantial increases in DM and DPN cases (873 and 846 cases, respectively). In contrast, the intervention model reduced the number of DM cases by 72.39% and DPN complications by 84.99%. Multidimensional determinants play a critical role in the development of DPN. Integrated, system-based interventions effectively reduce the disease burden. The proposed dynamic model offers a predictive framework for supporting evidence-based diabetes management and neuropathy control.

KEYWORDS
Diabetes Mellitus, Diabetic Polyneuropathy, Determinant, Dynamic System Model, Intervention

Cite This Paper in IEEE or APA Citation Styles
(a). IEEE Format:
[1] Susanti Dwi Ariani , Surya Hajar Fitria Dana , Ali Napiah Nasution , Dodik Tugasworo , Syaiful Ramadhan Harahap , "A System Dynamics Predictive Model for Diabetic Polyneuropathy Control Integrating Clinical and Socioeconomic Determinants," Universal Journal of Public Health, Vol. 14, No. 5, pp. 900 - 914, 2026. DOI: 10.13189/ujph.2026.140507.

(b). APA Format:
Susanti Dwi Ariani , Surya Hajar Fitria Dana , Ali Napiah Nasution , Dodik Tugasworo , Syaiful Ramadhan Harahap (2026). A System Dynamics Predictive Model for Diabetic Polyneuropathy Control Integrating Clinical and Socioeconomic Determinants. Universal Journal of Public Health, 14(5), 900 - 914. DOI: 10.13189/ujph.2026.140507.