Economic Burden of Measles among Hospitalized Children in Kenya

This retrospective study of 214 hospitalized children across 16 Kenyan public hospitals reveals that treating pediatric measles incurs a median societal cost of USD 176.18 per admission, primarily driven by staff and bed expenses, and places more than half of affected households at risk of catastrophic health expenditure, underscoring the urgent need for expanded insurance coverage and improved resource allocation.

The Gist

Imagine measles not just as a virus, but as a financial storm hitting a family's bank account. This paper is like a detailed weather report that measures exactly how much damage that storm causes to a family in Kenya and to the hospitals trying to help them.

Here is the story of the paper, broken down into simple parts:

1. The Mission: Counting the Cost of the Storm

Measles is a common problem in Kenya, especially for young children, because not enough kids are getting vaccinated. While we know the disease is dangerous, nobody had ever put a specific price tag on treating a child in a Kenyan hospital.

The researchers decided to act like accountants for the healthcare system. They looked back at records from 2013 to 2024 for 214 children who were sick enough to be admitted to 16 different public hospitals. Their goal was to answer two questions:

• How much does it cost the hospital to treat one child?
• How much does it cost the family (including travel, food, and lost work time)?

2. The Price Tag: A Heavy Bill

The researchers found that treating a child with measles is expensive, like buying a very pricey car part.

• The Hospital's Bill: On average, it costs the hospital about $139 (Kenyan Shillings) to treat one child.
• The Family's Bill: If you add in the family's travel, food, and the money they lose because a parent has to stay home to care for the sick child, the total cost jumps to about $176.

Who pays the most?
Think of the hospital bill as a pizza. The researchers found that the biggest slice (67%) is staff time. This means the doctors, nurses, and other workers spending time with the child are the most expensive part of the treatment. The second biggest slice (21%) is the hospital bed itself (the room, the electricity, the sheets). Medicine and lab tests are actually quite small slices of the pie.

3. The "What-If" Game: Sensitivity Analysis

The researchers played a "what-if" game to see if their numbers were solid. They asked: "What if nurses are so busy they can't spend as much time with patients, or what if they need to spend even more time?"

They found that if they adjusted the time nurses spend with patients to reflect real-world shortages, the cost would skyrocket by nearly 80%. This is like realizing your car repair bill was low because the mechanic only looked at the engine for 5 minutes; if they actually spent the full hour needed, the bill would be much higher.

4. The Family's Wallet: The "Catastrophic" Danger

This is the most worrying part of the story. The researchers asked: "If a family has to pay the full $176 bill out of their own pocket, what happens?"

They used a simulation (a computer model) to test families of different wealth levels.

• The Result: If a family has to pay the full cost, more than half of them would face "Catastrophic Health Expenditure."
• The Metaphor: Imagine a family's budget is a bucket of water. "Catastrophic" means the cost of the medicine is so big that it drains the bucket completely, leaving the family with nothing left for food or rent.
• The Reality: Even if the family only pays a small portion (like 20%) of the bill, many poor families in rural areas would still be in trouble. If they had to pay 100%, nearly 70% of urban families and 53% of rural families would be pushed into financial ruin.

5. Who Pays the Most? (The Sub-Groups)

The cost isn't the same for everyone. It's like a tiered pricing system:

• Age: The youngest children (under 1 year) cost the most to treat.
• Location: Families in cities (urban areas) pay more than those in the countryside, likely because city costs for food and transport are higher.
• Severity: Children who are referred from smaller clinics to big hospitals, or those with other sicknesses (like pneumonia) on top of measles, cost significantly more.
• Vaccination: Interestingly, the study found that whether a child was vaccinated or not didn't change the cost of treatment much, but unvaccinated children are the ones getting sick in the first place.

6. The Conclusion: What the Paper Says

The paper concludes that measles is a financial heavyweight. It doesn't just make children sick; it makes families poor.

The authors suggest that to stop families from going broke, the government and insurance systems need to cover the bill. They argue that we need to:

1. Expand insurance so families don't have to pay out of pocket.
2. Improve vaccination to stop the storm from starting in the first place.
3. Make hospitals more efficient to lower the staff and bed costs.

In short: Measles is a double-edged sword in Kenya—it hurts children's health and threatens to break their families' wallets. The only way to stop the financial bleeding is to prevent the disease and ensure the cost of treatment doesn't fall on the poorest families.

Technical Summary

Technical Summary: Economic Burden of Measles among Hospitalised Children in Kenya

Problem Statement
Despite the availability of safe and effective vaccines, measles remains an endemic public health concern in Kenya, characterized by recurrent outbreaks due to suboptimal vaccination coverage (82% for the first dose and 62% for the second as of 2024). While the disease burden is well-documented in terms of morbidity and mortality, there is a critical lack of published evidence quantifying the economic cost of treating hospitalised paediatric measles patients in Kenya. This gap hinders the ability of policymakers to understand the financial strain on households and the healthcare system, particularly regarding the risk of catastrophic health expenditure (CHE).

Methodology
The study employed a retrospective, prevalence-based cost-of-illness (COI) design using a bottom-up micro-costing approach.

• Data Source: Patient-level data was extracted from the Clinical Information Network (CIN), covering 214 children under 15 years of age hospitalised with measles across 16 public hospitals in Kenya between 2013 and 2024.
• Perspectives: Costs were estimated from two perspectives: the healthcare provider (direct medical costs only) and the societal perspective (direct medical, direct non-medical, and indirect costs).
• Costing Process: Resource quantities were derived from CIN data and expert opinion. Unit costs were sourced from the Kenya Medical Supplies Authority (KEMSA) price lists, facility surveys, market surveys, and existing literature. All costs were converted to 2025 Kenya Shillings (KES) and United States Dollars (USD) using specific exchange rates and GDP deflators.
• Analysis: A Generalised Linear Model (GLM) with a gamma distribution was used to estimate mean costs and 95% confidence intervals. Sub-group analyses examined variations by age, gender, geography, hospital level, and comorbidities. A one-way sensitivity analysis tested the robustness of estimates against variations in staff time, bed costs, and non-medical costs.
• Catastrophic Health Expenditure (CHE): A simulation-based approach was used to estimate the incidence of CHE, defined as out-of-pocket (OOP) spending exceeding 40% of a household's capacity to pay (non-food expenditure). This was simulated across six OOP payment scenarios (20% to 100% of total treatment cost) and across wealth quintiles.

Key Results

• Treatment Costs: The median cost per admission was USD 139.11 from the provider perspective and USD 176.18 from the societal perspective.
• Cost Drivers: Staff costs were the primary driver, accounting for 67% of provider costs and 53% of societal costs. Hospital bed days were the second largest contributor (21% provider, 17% societal).
• Sub-group Variations:
  • Age: Children aged 5 years and older incurred significantly lower costs (26% lower median) compared to those under 1 year.
  • Geography: Urban patients faced 43% higher median costs than rural patients.
  • Facility Level: Level 4 hospitals had significantly lower costs compared to Level 5 facilities.
  • Comorbidities: Patients with multiple comorbidities (e.g., pneumonia, gastrointestinal disorders) had significantly higher treatment costs.
  • County: Nairobi had the highest costs, while several other counties (e.g., Busia, Kakamega, Kilifi) reported significantly lower costs.
• Sensitivity Analysis: The total cost estimates were highly sensitive to staff time assumptions. Adjusting staff time to account for potential shortages increased median costs by 73% (societal) and 83% (provider). Variations in bed charges had a comparatively minor effect.
• Catastrophic Health Expenditure: Simulation results indicated that if households were required to pay the full treatment cost out-of-pocket, more than 50% of households could face CHE. Specifically, up to 53% of rural households and nearly 70% of urban households (across various wealth quintiles) were at risk of catastrophic spending if OOP payments reached 100% of the treatment cost.

Key Contributions
This study provides the first estimates of the cost of treating hospitalised paediatric measles patients in Kenya. It offers a comprehensive breakdown of cost drivers from both provider and societal perspectives, filling a significant evidence gap in low- and middle-income countries (LMICs). The study also quantifies the risk of financial impoverishment for households, demonstrating that measles treatment poses a severe economic threat even to those with moderate wealth levels.

Significance and Claims
The authors claim that the evidence generated is vital for policymakers to:

1. Refine Resource Allocation: Use cost data to better plan sub-national health budgets and resource distribution.
2. Expand Insurance Coverage: Justify the need for expanded health insurance coverage and targeted subsidies to shield households from out-of-pocket expenses.
3. Prioritize Vaccination: Provide economic justification for prioritizing investments in vaccination programs to reduce disease transmission and the associated treatment burden.
4. Inform Economic Evaluations: Serve as model inputs for researchers conducting economic evaluations of alternative measles vaccination strategies in Kenya and similar sub-Saharan settings.
5. Improve Efficiency: Enable hospitals to identify and address main cost drivers (staff and bed days) to reduce treatment expenses.

The paper concludes that while the economic burden is substantial, strategic interventions in financing and vaccination are necessary to mitigate the risk of catastrophic health expenditure and reduce the overall burden on the healthcare system.