DETERMINANTS AND CONSEQUENCES OF PATIENT AND HEALTH SYSTEM DELAYS IN TUBERCULOSIS DIAGNOSTICS AND TREATMENT AMONG INDIVIDUALS AGED 15 YEARS AND ABOVE AT KENYATTA NATIONAL HOSPITAL, NAIROBI COUNTY, KENYA

Technical Summary: Determinants and Consequences of Patient and Health System Delays in Tuberculosis Diagnosis and Treatment

Problem Statement
Tuberculosis (TB) remains a leading cause of morbidity and mortality globally, particularly in low- and middle-income countries. Despite advances in diagnostic and treatment strategies, control efforts are significantly hampered by delays in diagnosis and treatment initiation. These delays contribute to sustained transmission, increased disease severity, and poor treatment outcomes. The problem is multifaceted, involving "patient delay" (time from symptom onset to first healthcare contact) and "health system delay" (time from first contact to confirmed diagnosis). In Kenya, a high-burden setting, there is a need for context-specific evidence regarding the interaction between patient behavior, social determinants, and health system constraints to inform targeted interventions.

Methodology
The study employed a retrospective cohort design conducted at Kenyatta National Hospital (KNH), the largest national referral and teaching hospital in Kenya.

• Population: The study included 127 individuals aged ≥15 years with a confirmed diagnosis of pulmonary tuberculosis who were actively receiving treatment at KNH.
• Sampling: A systematic random sampling technique was used to select participants from the TB register. Purposive sampling was utilized for Key Informant Interviews (KIIs) to gather qualitative insights.
• Data Collection: Data were gathered through structured face-to-face interviews and retrospective clinical record reviews. The study reconstructed timelines from symptom onset to treatment initiation.
• Definitions:
  • Patient Delay: Time from symptom onset to first healthcare contact.
  • Health System Delay: Time from first contact to confirmed diagnosis.
  • Prolonged Delay: Defined as exceeding 14 days (consistent with global frameworks), though the study highlighted delays exceeding two months.
• Analysis: Quantitative data were analyzed using SPSS version 25. Descriptive statistics summarized characteristics. Chi-square tests assessed bivariate associations, and multivariable logistic regression was used to identify independent predictors of delay, reporting Adjusted Odds Ratios (AORs) with 95% Confidence Intervals (CIs). Qualitative data underwent thematic analysis.
• Ethical Considerations: The study received approval from relevant institutional review boards (University of Nairobi/KNH, Mount Kenya University, and NACOSTI). Informed consent was obtained, and confidentiality was maintained.

Key Results

• Demographics: The majority of participants were aged 40–49 years (32.3%), indicating a high burden among economically productive populations. Females constituted 51.2% of the sample. Most participants had attained primary (31.5%) or secondary (27.6%) education.
• Prevalence of Delay: Over half of the participants (59.8%) experienced prolonged patient delay, seeking care more than two months after symptom onset. Only 17.3% sought care within the first month.
• Barriers to Care:
  • Stigma: Perceived stigma was the most dominant barrier, reported by 96.9% of participants.
  • Fear: 66.9% reported fear following a potential diagnosis.
  • Geography: 31.5% of participants resided more than 10 km from a health facility.
• Care-Seeking Pathways: Participants frequently engaged in complex, non-linear pathways before reaching formal care. Common initial steps included self-medication, visits to pharmacies, and consultations with informal providers or traditional healers.
• Statistical Associations:
  • Bivariate Analysis: Significant associations were found between delay and age group (p = 0.032), distance to health facility (p = 0.015), and education level (p = 0.041). Sex was not significantly associated (p = 0.210).
  • Multivariable Analysis: After adjusting for confounders, no independent predictors remained statistically significant (all p > 0.05). This suggests that delays are driven by a complex interplay of factors rather than a single independent variable.

Key Contributions

1. Contextual Evidence: The study provides specific evidence from a major Kenyan tertiary facility regarding the magnitude of delays and the specific barriers faced by patients aged ≥15.
2. Pathway Mapping: It documents the fragmented nature of care-seeking, highlighting the role of informal providers and self-medication as critical points of delay before patients access formal diagnostic services.
3. Barrier Identification: The research quantifies the overwhelming role of psychosocial factors (stigma and fear) compared to structural factors (distance), noting that stigma affects nearly all participants.
4. Statistical Nuance: By demonstrating that bivariate associations disappear in multivariable models, the study underscores the complexity of TB delays, suggesting that interventions cannot rely on targeting single variables in isolation.

Significance and Claims
The paper claims that patient and health system delays remain substantial challenges in TB control, driven by a combination of behavioral, social, and structural factors.

• Socioeconomic Impact: The concentration of cases in the 40–49 age group highlights the dual burden of TB as both a biomedical and socioeconomic disease, affecting economically productive populations and leading to productivity losses.
• Intervention Needs: The authors argue that addressing delays requires a comprehensive, multi-sectoral approach. They assert that interventions must prioritize:
  • Community awareness and stigma reduction.
  • Improved access to healthcare services (decentralization).
  • Strengthening referral systems and integrating informal providers into TB control strategies.
• Limitations: The authors modestly acknowledge limitations, including potential recall bias regarding symptom onset, the restriction to a single tertiary facility (limiting generalizability), and the retrospective nature of the study. They conclude that while the study identifies key barriers, the lack of independent predictors in the regression model emphasizes the need for holistic, rather than siloed, interventions to reduce transmission and improve outcomes in high-burden settings.