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

This 2022 cross-sectional study at Kenyatta National Hospital identifies significant patient and health system delays in tuberculosis diagnosis and treatment among adults, driven by factors such as facility distance, socioeconomic constraints, fear, and stigma, thereby underscoring the urgent need for decentralized services and improved health system responsiveness to accelerate case detection.

The Gist

Imagine Tuberculosis (TB) as a silent thief that breaks into your home (your lungs) and starts stealing your health. The only way to catch this thief is to call the police (the doctors) immediately. But in this study, researchers at Kenyatta National Hospital (KNH) in Nairobi found that for many people, the call to the police is happening way too late.

Here is the story of the study, broken down into simple parts using everyday analogies.

1. The Big Picture: The "Late Arrival" Problem

The study looked at 127 adults who finally got diagnosed with TB. The researchers wanted to know: How long did it take them to get help, and why did they wait?

Think of the journey to getting cured as a relay race.

• Leg 1 (Patient Delay): The time from when the person first feels sick until they decide to go to the doctor.
• Leg 2 (System Delay): The time from when they walk into the clinic until they actually start taking the medicine.

The study found that both legs of the race were too slow. Many people waited months before seeking help, and even after they arrived, the system sometimes made them wait longer.

2. Who Was Running the Race? (The People)

The people in this study were mostly adults in their prime working years (ages 40–49), with a mix of men and women.

• The "Busy" Crowd: Many were self-employed or professionals. They are like people juggling too many balls; when they get sick, they might try to keep juggling instead of stopping to fix the problem.
• The "Struggling" Crowd: A significant number were unemployed or housewives. For them, the race was harder because they often lacked the money for transport or the confidence to speak up.

3. Why Did They Wait? (The Roadblocks)

The researchers found three main reasons why people didn't run to the doctor immediately:

• The "It's Just a Cold" Trap: Many people thought their cough was just a minor cold or flu. They tried home remedies or bought cheap medicine from a local shop first. It's like trying to fix a leaking roof with a bucket instead of calling a roofer. By the time they realized it was a serious leak (TB), the damage was done.
• The "Scary Monster" Fear: When people finally got diagnosed, 67% felt scared, and 97% felt like their community would judge them. TB is still seen as a "shameful" disease in many places. It's like having a secret that everyone might whisper about. This fear kept people hiding in the shadows instead of seeking help.
• The "Long Walk" Barrier: For some, the hospital was just too far away. If you live more than 10 kilometers from the clinic, the cost of a bus ticket and the time lost from work become a huge barrier. It's like trying to run a marathon when your shoes are too heavy.

4. The Hospital Experience: The "Traffic Jam"

Once the patients finally arrived at the hospital (Kenyatta National Hospital), they faced a different kind of delay.

• The System Bottleneck: Even though the hospital is the biggest in the country, it was overwhelmed. Patients had to wait in long lines, go through multiple tests, and navigate a complex maze of referrals.
• The "Free" Expectation: Almost everyone (96%) believed TB treatment should be free. While the government provides free treatment, the process of getting there can feel expensive due to lost wages and transport costs.

5. The Results in Numbers (The Scoreboard)

• The Wait: Nearly 30% of people waited 2 to 3 months after symptoms started before seeing a doctor. Another 33% waited 5 months or longer. That is a long time for a thief to stay in your house!
• The Stigma: Almost everyone felt that society looks down on TB patients.
• The Winners: People who lived closer to the hospital and had a steady job tended to get help faster.

6. What Should We Do? (The Game Plan)

The study suggests we need to change the rules of the game to catch the thief sooner:

1. Turn on the Lights (Awareness): We need to tell people, "If you cough for more than two weeks, don't ignore it. It's not just a cold; it's TB, and it's curable."
2. Remove the Stigma (Kindness): We need to treat TB patients with respect, not fear. If people aren't afraid of being judged, they will come out of the shadows sooner.
3. Bring the Clinic Closer (Access): Instead of making sick people travel far, we should bring testing services to their neighborhoods.
4. Speed Up the Line (Efficiency): Hospitals need to streamline their processes so patients don't get stuck in traffic jams of paperwork and waiting.

The Bottom Line

This study is a wake-up call. In Kenya, the fight against TB is being slowed down not just by the bacteria itself, but by fear, distance, and confusion. If we can make the path to the doctor shorter, cheaper, and less scary, we can stop the thief before it steals too much health from our communities.

Technical Summary

Title

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

1. Problem Statement

Tuberculosis (TB) remains a critical public health challenge in Kenya, a high-burden country with an estimated incidence of 256 cases per 100,000 people. Despite global efforts, delays in diagnosis and treatment initiation continue to drive transmission, worsen clinical outcomes, and increase mortality. These delays are categorized into:

• Patient Delay: The time from symptom onset to the first visit to a health facility.
• Health-System Delay: The time from the first facility visit to the initiation of treatment.

The post-COVID-19 era has further strained health systems, potentially exacerbating these delays. While previous studies have addressed delays in primary and secondary facilities, there is a lack of evidence regarding tertiary care centers like Kenyatta National Hospital (KNH), which manages complex and advanced cases. Understanding the specific determinants of delay in this high-volume referral setting is crucial for optimizing national TB control strategies.

2. Methodology

• Study Design: Descriptive cross-sectional study.
• Setting: Kenyatta National Hospital (KNH), Nairobi, Kenya (the country's largest referral and teaching hospital).
• Study Population: 127 adults (≥15 years) newly diagnosed with smear-positive pulmonary TB who had been on treatment for less than six months.
  • Exclusion Criteria: Retreatment cases, extrapulmonary TB, and residents outside Nairobi County.
• Sampling: Systematic sampling was used to recruit participants from patient registration records (n=128 target, n=127 analyzed).
• Data Collection: Structured, interviewer-administered questionnaires collected data on:
  • Socio-demographics (age, sex, education, occupation, household size).
  • Care-seeking pathways (first point of contact, reasons for delay).
  • Clinical history (symptom duration, comorbidities).
  • Psychosocial factors (stigma, fear, perception of free care).
• Variables:
  • Dependent: Patient delay, Health-system delay, and Total delay.
  • Independent: Demographic factors, distance to facility, occupation, and health-worker interactions.
• Statistical Analysis:
  • Descriptive statistics (frequencies, medians, IQR).
  • Chi-square tests for bivariate associations.
  • Multivariable logistic regression to identify independent predictors (Adjusted Odds Ratios - AOR).
  • Significance level set at p < 0.05.

3. Key Results

A. Demographic Profile

• Gender: Nearly equal distribution (51.2% Female, 48.8% Male).
• Age: The largest cohort was aged 40–49 years (32.3%), followed by 30–39 years (21.3%).
• Education: 59.1% had primary/secondary education; 26.8% had no formal education.
• Occupation: Predominantly self-employed (36.2%) and professionals (32.3%); 13.4% were unemployed.

B. Care Pathway and Psychosocial Factors

• Diagnosis Location: 92.9% were first diagnosed in government facilities; only 7.1% in private facilities.
• Psychological Impact: 66.9% felt scared upon diagnosis.
• Stigma: 96.9% perceived TB-related discrimination in their communities.
• Cost Perception: 96.1% believed TB treatment should be free.

C. Delay Quantification

• Patient Delay: Significant delays were observed.
  • 29.9% sought care after 2–3 months.
  • 33.1% delayed for 5 months or longer.
  • 14.2% delayed for over 7 months.
• Health-System Delay: Delays varied by initial facility type, with hospitals showing longer median delays compared to clinics and pharmacies.

D. Determinants of Delay (Statistical Associations)

• Significant Bivariate Associations:
  • Age: Significantly associated with time to first treatment (p = 0.003).
  • Distance: Distance to the facility was significantly associated with the location of diagnosis (p = 0.004) and time to seek treatment (p = 0.047).
  • Household Size: Number of children was associated with perceptions on reducing delays (p < 0.001).
• Multivariable Analysis (Independent Predictors):
  • Occupation: Unemployed and housewife participants showed higher odds of delay (though not statistically significant in the final adjusted model, trends were strong: AOR 2.12 and 2.75 respectively).
  • Distance: Living >10 km from the facility showed a strong trend toward delay (AOR = 4.75, p = 0.09).
  • Note: While household size and education were significant in bivariate analysis, they were not independent predictors in the multivariable model, suggesting occupation and geography are the primary drivers when controlling for other factors.

4. Key Contributions

1. Context-Specific Evidence: Provides the first detailed analysis of TB delays specifically within a major Kenyan tertiary referral hospital (KNH) in the post-pandemic context.
2. Quantification of Delays: Establishes that a significant proportion of patients at a tertiary level still experience prolonged patient delays (often >2 months), indicating that referral to a top-tier hospital does not guarantee early detection.
3. Identification of Structural Barriers: Highlights that geographic distance and socio-economic status (specifically unemployment and dependency) are critical structural barriers, even in a setting where treatment is theoretically free.
4. Stigma Assessment: Quantifies the pervasive nature of TB-related stigma (96.9%), identifying it as a major psychosocial barrier to care-seeking.

5. Significance and Recommendations

The study underscores that despite free treatment availability at KNH, systemic and behavioral bottlenecks prevent timely TB control.

• Decentralization: There is an urgent need to decentralize diagnostic services to reduce the travel burden for patients living >10 km from major hospitals.
• Targeted Awareness: Interventions must target middle-aged adults (40–49 years) and unemployed populations to improve symptom recognition and health-seeking behavior.
• Stigma Reduction: Comprehensive anti-stigma campaigns at both community and facility levels are essential to reduce fear and discrimination.
• System Efficiency: KNH and similar tertiary centers must optimize referral pathways and diagnostic turnaround times to minimize health-system delays.
• Socio-Economic Support: Integrating economic support for low-income and unemployed patients could mitigate the financial barriers that contribute to delayed care.

Conclusion: Significant patient and system-level delays persist at KNH. Addressing these requires a multi-pronged approach focusing on geographic accessibility, socio-economic support, and aggressive stigma reduction to align with WHO End TB Strategy targets.