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Utilization of TB control services in Kenya

Utilization of TB control services in Kenya. Analysis of wealth inequalities. Christy Hanson, PhD, MPH World Health Organization Stop TB Department. Trends in Tuberculosis: Kenya. 62.3% of population lives on <$2/day (1994) 50+% of TB patients are HIV+.

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Utilization of TB control services in Kenya

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  1. Utilization of TB control services in Kenya Analysis of wealth inequalities Christy Hanson, PhD, MPH World Health Organization Stop TB Department

  2. Trends in Tuberculosis: Kenya • 62.3% of population lives on <$2/day (1994) • 50+% of TB patients are HIV+ Source: WHO reports: 1997, 1998, 1999, 2000,2001

  3. 700 0.16 0.14 600 0.12 500 0.10 400 0.08 300 0.06 200 0.04 100 0.02 0 0.00 1980 1990 2010 2000 TB and HIV in Kenya HIV prevalence TB incidence Source: B. Williams, WHO Geneva

  4. Where the system provides DOTS 88% of Kenyans with illness sought care from formal sector

  5. Study objectives • Current performance of health sector in reaching poor • Treatment seeking patterns of poor vs. non-poor • Identify provider and patient characteristics associated with utilization of DOTS providers

  6. Survey implementation Sampling Frame • 1 district per province • 20% of all facilities/pharmacies: public, private, NGO • N=3500 4 points in service delivery • Outpatient (TB symptomatic) • n=1750 • Diagnostic (TB suspect) • n=675 • Treatment: initial phase (TB patient) • n=540 • Treatment: completion phase (cured TB case)

  7. Survey Tools • Provider: costs, services, patient base • Individual • Demographic information • Health information • Symptoms, choice set (providers that patients perceive are accessible) • TB knowledge • Treatment-seeking behavior • Movement between formal, informal, private, public • Utilization and expenditures • Valuation • Inventory what is important in decision-making • Preferences

  8. Analytical techniques • Asset-index used for measuring wealth • Transition matrices • Logistic regression: individual factors • Conditional logit (McFadden’s): provider characteristics • Define individual choice set

  9. Profile of TB patients treated in public and private sectors 3% of patients completing treatment are among the poorest quintile

  10. Expected vs. actual utilization distribution

  11. Change in wealth profile along continuum of diagnosis & treatment

  12. Movement through the health system: the case of the poor • 40% start at decentralized dispensaries • Almost equal % in public / private • Those who start at hospital level, 12% transition “backwards” • Less efficient transitioning • More visits (half had 5-10 visits, still not referred for dx) • More time ill • Higher expenditures • Most interact with a “DOTS” facility within 1st three visits, still don’t get referred for diagnosis • Individual & provider factors behind transitioning

  13. Where patients go vs. Where the system provides DOTS

  14. Factors associated with selection of public sector DOTS provider as 1st choice Poor Individual characteristics • Ability to pay in kind, negotiate price (Q1 only) • Perception of DOTS facility as best quality • Knowledge of fees (negative association) Non-poor Individual characteristics • Know TB treatment is free in public sector (35% knew) • Confidentiality • Availability of medicine • Waiting time • Perception of public DOTS facility as best quality • Knowledge of fees (negative association)

  15. Conclusions & Next steps • TB patients actively seeking care • System passive in referral, detection • Poor disproportionately represented at all stages • Research: prevalence distribution by wealth • Social science research: why? • Private sector: competitive, well used • Define comparative advantage of NLTP • Public system subsidizing non-poor • Not effectively supporting poor • District variance: lessons to be learned from successful districts

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