A Cluster-Based Predictive Model of Tuberculosis Spatial Distribution as a Basis for Evaluating Community-Based Case Finding Program Management
DOI:
https://doi.org/10.35326/ijmp.v6i2.8035Keywords:
Tuberculosis, Spatial Clustering, Predictive Model, Community-Based Case Finding, Program ManagementAbstract
Tuberculosis (TB) remains a major public health challenge, particularly in urban and community settings where transmission risk varies spatially across regions. Conventional TB control programs often rely on aggregate indicators, which may obscure localized risk patterns and limit the effectiveness of community-based case finding interventions. This study aims to develop a cluster-based predictive model of TB spatial distribution and to utilize the model as a basis for evaluating the management of the Community-Based TB Case Finding Program. This study employed a quantitative observational design with a spatial–predictive approach. Secondary data on notified TB cases and program performance indicators were collected from primary health centers and local health offices. Spatial cluster analysis was conducted to identify TB hotspot and coldspot areas, followed by predictive modeling to classify regions into high-, medium-, and low-risk categories. The clustering results were then integrated with program indicators to evaluate planning, implementation, and resource allocation across regions. The results demonstrate that TB distribution forms significant spatial clusters, indicating non-random patterns of risk across subdistricts. The predictive model effectively classified regional TB risk and revealed mismatches between high-risk areas and program intervention intensity. Furthermore, substantial variations in screening coverage, community health worker activities, and referral processes were identified across risk clusters. In conclusion, the cluster-based predictive model proved effective as a tool for evaluating and strengthening the management of community-based TB case finding programs. This approach supports more targeted, risk-based interventions and enhances evidence-based decision-making in TB control.
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