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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 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+ Source: WHO reports: 1997, 1998, 1999, 2000,2001
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
Where the system provides DOTS 88% of Kenyans with illness sought care from formal sector
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
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)
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
Analytical techniques • Asset-index used for measuring wealth • Transition matrices • Logistic regression: individual factors • Conditional logit (McFadden’s): provider characteristics • Define individual choice set
Profile of TB patients treated in public and private sectors 3% of patients completing treatment are among the poorest quintile
Change in wealth profile along continuum of diagnosis & treatment
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
Where patients go vs. Where the system provides DOTS
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)
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