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 Source: WHO reports: 1997, 1998, 1999, 2000, % of population lives on <$2/day (1994) 50+% of TB patients are HIV+

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= 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

Expected vs. actual utilization distribution

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 1 st 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 1 st 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