Spatial epidemiology of tuberculosis diagnostic delays, healthcare access disparities, and socioeconomic inequities in Nairobi County, Kenya - David Majuch Kunjok; John Gachohi Mwangi; Salome Kairu-Wanyoike; Johnson Kinyua; Susan Mambo (PLoS ONE, 2025)
Introduction Kenya ranks among the top 30 countries with a high tuberculosis (TB) burden globally. With a TB prevalence of 558 per 100,000, only 46% of TB cases are diagnosed and treated, leaving 54% undiagnosed and at risk of spreading the disease. This study analyzed the spatial distribution of tuberculosis diagnostic delays and their association with health care accessibility and socioeconomic inequalities in Nairobi County, Kenya. Materials and methods The cross-sectional study included 222 newly diagnosed bacteriologically confirmed Mycobacterium tuberculosis (Mtb) patients from Mbagathi County Hospital (MCH), Mama Lucy Kibaki Hospital (MLKH), and Rhodes Chest Clinic (RCC) in Nairobi County, Kenya. Patients were recruited consecutively through census sampling and categorized into two groups: delayed diagnosis (≥21 days from symptom onset) and non-delayed (<21 days) as defined by the WHO cutoff point. Patients’ residential locations were georeferenced using handheld GPS devices and captured digitally via Kobo Collect. Spatial analyses were performed using ArcGIS Pro, version, where Global Moran’s I statistic was used to assess spatial autocorrelation in the distribution of TB cases. Result Spatial analyses identified 28 statistically significant clusters of delayed TB diagnoses within Nairobi County. Spatial autocorrelation analysis using Moran’s I revealed a significant clustered distribution (Moran’s Index = 0.471, z-score = 3.370, p < 0.001). Hotspot analysis with the Getis-Ord Gi* statistic detected high-delay clusters (z > 2.58, p < 0.001) in informal settlements. Discussion and conclusion The study revealed significant spatial clustering of delayed TB diagnoses in Nairobi County, particularly in informal settlements. In contrast, timely diagnoses were predominantly clustered in high-income areas like Lang’ata and Karen. These clusters were significantly associated with lower household income and increased travel time to health facilities which underscored the need for targeted implementation of TB diagnostic services and control measures in the wards with the highest delays.