utilization of tb control services in kenya analysis of wealth inequalities christy hanson, phd, mph...
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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
Infectious (smear+) cases of TB
010,00020,00030,00040,00050,00060,00070,00080,000
1995 1996 1997 1998 1999 2000
Estimated sm+ cases(incidence)
Sm+ cases detected
Source: WHO reports: 1997, 1998, 1999, 2000,2001
• 62.3% of population lives on <$2/day (1994)
• 50+% of TB patients are HIV+
TB and HIV in Kenya
0
100
200
300
400
500
600
700
1980 1990 2000 20100.000.020.040.060.080.100.120.140.16
HIV
pre
vale
nce
TB
inci
den
ce
Source: B. Williams, WHO Geneva
Where the system provides DOTS
Health facilities by type,2001
Health centre12%
Hospital10%
Dispensary61%
Nursing home4%
Health clinic13%
0
20
40
60
80
100
Hospital Health centre Dispensary
Percent of all public facilities that participate in DOTS implementation
2001
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
0
10
20
30
40
50
60
% o
f al
l pat
ient
s
Q1:
poor
est
Q2
Q3
Q4
Q5:
wea
lthi
est
Wealth Quintiles
Public sector: initiating tx
Private sector: initiating tx
Public sector: completing tx
Private sector: completing tx
3% of patients completing treatment are among the poorest quintile
Expected vs. actual utilization distribution
01020304050607080
% of patients surveyed
Q1-Q3: Poorest Q4-Q5: Non-poor
Wealth quintiles
Expected
Symptomatics
Suspects
New cases
Cases completing tx
Change in wealth profile along continuum of diagnosis & treatment
0
1
2
3
4
5
mea
n w
ealt
h s
core
Stage in treatment process
Nairobi
TB symptomatics
Diagostics; TB suspects
New PTB (1st month of tx)
0
0.5
1
1.5
2
2.5
mea
n w
ealt
h s
core
S tage in treatment process
Nyeri
Diagnostics
New PTB (1st month)
Completing 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
0
5
10
15
20
25
dispensary healthcentre
hospital pharmacy
First interaction with health system
public private
0
20
40
60
80
100
Hospital Health centre Dispensary
Percent of all public facilities that implement DOTS
2001
Health facilities by type,2001
Dispensary61%
Health centre12%
Hospital10%
Nursing home4%
Health clinic13%
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
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