Development of a Composite Performance Score Tool for Diabetes Mellitus Management: Features and Framework

<i>Background:</i> Type 2 diabetes mellitus (T2DM) remains a critical public health challenge in low- and middle-income countries (LMICs), where existing assessment tools often fail to integrate the full spectrum of clinical, behavioral, and contextual determinants of care. This study developed and validated the Management Performance Composite Scoring Tool (MPCST), a multidimensional instrument designed for primary healthcare (PHC) settings, grounded in the Chronic Care Model (CCM). <i>Methods:</i> An exploratory sequential mixed-methods design was conducted over four months across five public PHC facilities in Nairobi, Kenya. The qualitative phase involved six focus group discussions (n=52) and ten key informant interviews to identify domains relevant to T2DM management. In the quantitative phase, 181 adults with stable T2DM were selected via systematic random sampling. Data collection employed validated instruments alongside standardized clinical and biochemical assessments. A composite scoring algorithm was developed using statistical machine learning and deployed in a Streamlit-based application for real-time clinical utility. Psychometric validation included content validity (Lawshe’s method), generalizability theory, and convergent and concurrent validity analysis. <i>Results:</i> Among 181 participants (63.5% female; mean age 55.9 years), glycemic control was poor (mean HbA1c: 9.39 mmol/L), and lifestyle performance was suboptimal. Tool development incorporated clinical (e.g., HbA1c, BMI), behavioral (diet, activity, distress), and contextual (social determinants, healthcare access) domains. Ordinal regression yielded excellent model fit (<i>pseudo R²</i> = 0.949, AIC = 69.821). Geometric mean aggregation improved behavioral sensitivity, while minimax strategies optimized clinical indicator selection. The MPCST demonstrated high reliability (G = 0.8351), strong convergent (<i>r</i> = 0.74) and concurrent (<i>r</i> = 0.565) validity. <i>Conclusion:</i> The MPCST is a rigorously validated, contextually relevant, and scalable tool for evaluating T2DM care quality in LMIC PHC systems. Its dual-format (manual and app-based) design supports routine use, longitudinal tracking, and integration into health systems, thereby enhancing clinical decision-making and patient-centered care.

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Journal
International Journal of Diabetes and Endocrinology
Published
2025-12-09
DOI
https://doi.org/10.11648/j.ijde.20251004.13
Primary Topic
Diabetes Management and Education
Type
article
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article

Development of a Composite Performance Score Tool for Diabetes Mellitus Management: Features and Framework

Isaac Okeyo, Alice Ojwang, Francis Oguya, Frederick C. F. Otieno et al.
International Journal of Diabetes and Endocrinology
Diabetes Management and Education
article

Development of a Composite Performance Score Tool for Diabetes Mellitus Management: Features and Framework

Isaac Okeyo, Alice Ojwang, Francis Oguya, Frederick C. F. Otieno, Argwings Kodhek
article en

Abstract

<i>Background:</i> Type 2 diabetes mellitus (T2DM) remains a critical public health challenge in low- and middle-income countries (LMICs), where existing assessment tools often fail to integrate the full spectrum of clinical, behavioral, and contextual determinants of care. This study developed and validated the Management Performance Composite Scoring Tool (MPCST), a multidimensional instrument designed for primary healthcare (PHC) settings, grounded in the Chronic Care Model (CCM). <i>Methods:</i> An exploratory sequential mixed-methods design was conducted over four months across five public PHC facilities in Nairobi, Kenya. The qualitative phase involved six focus group discussions (n=52) and ten key informant interviews to identify domains relevant to T2DM management. In the quantitative phase, 181 adults with stable T2DM were selected via systematic random sampling. Data collection employed validated instruments alongside standardized clinical and biochemical assessments. A composite scoring algorithm was developed using statistical machine learning and deployed in a Streamlit-based application for real-time clinical utility. Psychometric validation included content validity (Lawshe’s method), generalizability theory, and convergent and concurrent validity analysis. <i>Results:</i> Among 181 participants (63.5% female; mean age 55.9 years), glycemic control was poor (mean HbA1c: 9.39 mmol/L), and lifestyle performance was suboptimal. Tool development incorporated clinical (e.g., HbA1c, BMI), behavioral (diet, activity, distress), and contextual (social determinants, healthcare access) domains. Ordinal regression yielded excellent model fit (<i>pseudo R²</i> = 0.949, AIC = 69.821). Geometric mean aggregation improved behavioral sensitivity, while minimax strategies optimized clinical indicator selection. The MPCST demonstrated high reliability (G = 0.8351), strong convergent (<i>r</i> = 0.74) and concurrent (<i>r</i> = 0.565) validity. <i>Conclusion:</i> The MPCST is a rigorously validated, contextually relevant, and scalable tool for evaluating T2DM care quality in LMIC PHC systems. Its dual-format (manual and app-based) design supports routine use, longitudinal tracking, and integration into health systems, thereby enhancing clinical decision-making and patient-centered care.

International Journal of Diabetes and EndocrinologyVol. 10(4)
University of Nairobi (KE), Technical University of Kenya (KE)
Openalex Percentile: Top 48%
Diabetes Management and Education
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