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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


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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

slide2

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

tb and hiv in kenya

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

slide4

Where the system provides DOTS

88% of Kenyans with illness sought care from formal sector

slide5

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
slide6

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)
slide7

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
Analytical techniques
  • Asset-index used for measuring wealth
  • Transition matrices
  • Logistic regression: individual factors
  • Conditional logit (McFadden’s): provider characteristics
    • Define individual choice set
slide9

Profile of TB patients treated in public and private sectors

3% of patients completing treatment are among the poorest quintile

slide12

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
slide13

Where patients go vs.

Where the system provides DOTS

slide14

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
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