RBF PROGRAMS PERFORMANCE –
A CROSS COUNTRY OVERVIEW FROM
OPERATIONAL DATA
BUENOS AIRES, MARCH 2014
OBJECTIVE
To provide a cross-country overview of
performance to assess progress and identify areas
for further inquiries
2
COUNTRY PROGRAMS IN REVIEW
Country Start date Program areas Catchment population
Benin Mar 2012 8 districts 2.2 million (2...
UNDERSTANDING OPERATIONAL DATA
Verified data from contracting facilities
 Not available for non-participating facilities ...
ESTIMATED COVERAGE OF
SELECTED KEY SERVICES
5
COVERAGE OF INSTITUTIONAL DELIVERIES
-
10
20
30
40
50
60
70
80
90
100
2 3 4 1 2 3 4 1 2 3 4 1 2 3 4
2010 2011 2012 2013
Bu...
COVERAGE OF INSTITUTIONAL DELIVERIES
Each bar represents a quarter of implementation
0
10
20
30
40
50
60
70
80
90
100
Beni...
COVERAGE OF POSTNATAL CARE
0
10
20
30
40
50
60
70
80
90
100
2 3 4 1 2 3 4 1 2 3 4 1 2 3
2010 2011 2012 2013
Benin
Burkina ...
COVERAGE OF FULL VACCINATION AMONG
CHILDREN AGED 1 AND UNDER
%
-
20
40
60
80
100
120
2 3 4 1 2 3 4 1 2 3 4 1 2 3
2010 2011...
MODERN FAMILY PLANNING USE AMONG
WOMEN OF REPRODUCTIVE AGE
%
%
-
10
20
30
40
50
60
70
80
90
2 3 4 1 2 3 4 1 2 3 4 1 2 3
20...
ANNUALIZED RATE OF CURATIVE CARE
OUTPATIENT CONTACT (PER CAPITA)
-
0.50
1.00
1.50
2.00
2 3 4 1 2 3 4 1 2 3 4 1 2 3
2010 20...
TRENDS IN QUALITY
12
TOTAL QUALITY SCORE IN HEALTH FACILITIES
0
10
20
30
40
50
60
70
80
90
100
Burkina
Faso
Benin Cameroon Kenya Nigeria Zambia...
TRENDS IN KEY COMPONENT QUALITY
SCORES
-
10
20
30
40
50
60
70
80
90
100
4 1 2 3 4 1 2
2011 2012 2013
%
Drugs management
Be...
KEY ISSUES IN
PROGRAM PAYMENT
15
PER CAPITA RBF PAYMENT ON SERVICE
DELIVERY PER YEAR
Paymentpercapita(US$)
“Year” means complete 12 calendar months countin...
SHARE OF PAYMENT TO HEALTH CENTERS AND
LOWER LEVEL IN TOTAL PAYMENT ON SERVICE
DELIVERY
%
Figures reported are averages of...
THREE SERVICES ABSORBING LARGEST
SHARE OF PAYMENT
OP >5
11%
OP
<=5
15%
Inst.
Delive
ries
17%
Other
s
57%
Burundi
Zambia
Ca...
KEY SUMMARY POINTS
1. There is a large variation in programs performance, both at
baseline and over time
2. Overall, there...
STRENGTHENING WORKS ON
ADMINISTRATIVE DATA
1. Regularly monitoring program progress to identify
candidates for adjustment ...
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Annual Results and Impact Evaluation Workshop for RBF - Day One - RBF Programs Performance - A Cross-Country Overview from Operational Data

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A presentation from the 2014 Annual Results and Impact Evaluation Workshop for RBF, held in Buenos Aires, Argentina.

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Annual Results and Impact Evaluation Workshop for RBF - Day One - RBF Programs Performance - A Cross-Country Overview from Operational Data

  1. 1. RBF PROGRAMS PERFORMANCE – A CROSS COUNTRY OVERVIEW FROM OPERATIONAL DATA BUENOS AIRES, MARCH 2014
  2. 2. OBJECTIVE To provide a cross-country overview of performance to assess progress and identify areas for further inquiries 2
  3. 3. COUNTRY PROGRAMS IN REVIEW Country Start date Program areas Catchment population Benin Mar 2012 8 districts 2.2 million (22%) Burkina Faso Dec 2011 3 districts 813 thousand (5%) Burundi Mar 2010 Countrywide 9.8 million (100%) Cameroon Littoral: Apr 2011 3 other: Jul 2012 4 regions 2.8 million (13%) Kenya Dec 2011 1 sub-county 200 thousand (0.5%) Nigeria Dec 2011 3 LGAs 416 thousand (0.2%) Zambia Apr 2012 11 districts 1.5 million (11%) Zimbabwe Mar 2012 18 districts 4.2 million (30%) Afghanistan April 2009 11 provinces Laos Mar 2013 5 provinces 2.2 million (33%) Sierra Leone Oct 2010 13 districts 5.9 million (100%) Total population is for 2012 (WDI) 3
  4. 4. UNDERSTANDING OPERATIONAL DATA Verified data from contracting facilities  Not available for non-participating facilities (private sector, hospitals in some cases)  Do not reflect true population coverage  Usually not available for control groups or for non-incentivized indicators (have to rely on HMIS)  May still be prone to errors despite verification  Not IE, (lack of) effect is suggestive but not conclusive Lack of precise information to calculate coverage  Size of catchment population  Parameters to calculate population “at risk” (i.e., denominators) Cross-country analysis issues  Challenges in (lack of) comparability of definitions and designs  Only looks at program level, not sub-program level 4
  5. 5. ESTIMATED COVERAGE OF SELECTED KEY SERVICES 5
  6. 6. COVERAGE OF INSTITUTIONAL DELIVERIES - 10 20 30 40 50 60 70 80 90 100 2 3 4 1 2 3 4 1 2 3 4 1 2 3 4 2010 2011 2012 2013 Burundi % 6
  7. 7. COVERAGE OF INSTITUTIONAL DELIVERIES Each bar represents a quarter of implementation 0 10 20 30 40 50 60 70 80 90 100 Benin B Faso Cameroon Kenya Nigeria Zambia Zimbabwe % 7
  8. 8. COVERAGE OF POSTNATAL CARE 0 10 20 30 40 50 60 70 80 90 100 2 3 4 1 2 3 4 1 2 3 4 1 2 3 2010 2011 2012 2013 Benin Burkina Faso Nigeria Zambia Zimbabwe Burundi % 8
  9. 9. COVERAGE OF FULL VACCINATION AMONG CHILDREN AGED 1 AND UNDER % - 20 40 60 80 100 120 2 3 4 1 2 3 4 1 2 3 4 1 2 3 2010 2011 2012 2013 Burundi Benin B Faso Cameroon Kenya Nigeria Zambia Zimbabwe 9
  10. 10. MODERN FAMILY PLANNING USE AMONG WOMEN OF REPRODUCTIVE AGE % % - 10 20 30 40 50 60 70 80 90 2 3 4 1 2 3 4 1 2 3 4 1 2 3 2010 2011 2012 2013 Burundi Benin B Faso Cameroon Kenya Nigeria Zimbabwe 0 5 10 15 20 25 30 4 1 2 3 4 1 2 3 2011 2012 2013 10
  11. 11. ANNUALIZED RATE OF CURATIVE CARE OUTPATIENT CONTACT (PER CAPITA) - 0.50 1.00 1.50 2.00 2 3 4 1 2 3 4 1 2 3 4 1 2 3 2010 2011 2012 2013 Burundi Benin B Faso Cameroon Nigeria Zambia Zimbabwe - 0.20 0.40 0.60 0.80 1.00 1.20 1 2 3 4 1 2 3 2012 2013 11
  12. 12. TRENDS IN QUALITY 12
  13. 13. TOTAL QUALITY SCORE IN HEALTH FACILITIES 0 10 20 30 40 50 60 70 80 90 100 Burkina Faso Benin Cameroon Kenya Nigeria Zambia Zimbabwe % Scores are averages of health centers and hospitals, technical and subjective where applicable Each bar represents a quarter of implementation 13
  14. 14. TRENDS IN KEY COMPONENT QUALITY SCORES - 10 20 30 40 50 60 70 80 90 100 4 1 2 3 4 1 2 2011 2012 2013 % Drugs management Benin Nigeria - 10 20 30 40 50 60 70 80 90 100 4 1 2 3 4 1 2 2011 2012 2013 % Financial management Benin Nigeria 14
  15. 15. KEY ISSUES IN PROGRAM PAYMENT 15
  16. 16. PER CAPITA RBF PAYMENT ON SERVICE DELIVERY PER YEAR Paymentpercapita(US$) “Year” means complete 12 calendar months counting from the month when program started Value for the most recent year is extrapolated if duration is less than 12 months Payment components consist of quantity, quality, and equity bonus where applicable 0 0.5 1 1.5 2 2.5 3 3.5 Kenya Cameroon B Faso Nigeria Zambia Benin Zimbabwe Burundi Year 1 Year 2 Year 3 Year 4 16
  17. 17. SHARE OF PAYMENT TO HEALTH CENTERS AND LOWER LEVEL IN TOTAL PAYMENT ON SERVICE DELIVERY % Figures reported are averages of all quarters to date 0 10 20 30 40 50 60 70 80 90 100 Kenya Zambia Nigeria Burkina Faso Benin Zimbabwe Burundi Cameroon 17
  18. 18. THREE SERVICES ABSORBING LARGEST SHARE OF PAYMENT OP >5 11% OP <=5 15% Inst. Delive ries 17% Other s 57% Burundi Zambia Cameroon Zimbabwe OP contac t 6% Inst. Delive ries 35% FP 40% Others 19% OP contact 35% Inst. Deliveri es 15% FP 21% Others 29% OPC 21% Hosp. days 15% VCT 12% Other s 52% Figures reported are averages of all quarters to date 18
  19. 19. KEY SUMMARY POINTS 1. There is a large variation in programs performance, both at baseline and over time 2. Overall, there has been good progress in performance of key services and quality as measured by the programs 3. Annual per capita payment ranges from US$ 0.3 to US$3. Some “one dollar per capita” programs perform rather well (Zambia, Nigeria) 4. Curative care (especially OP), institutional deliveries, and FP are typically the largest cost items 5. Most programs place strong emphasis on the low level of health system (health centers and below) 6. Some areas requiring further investigations: - Consistently slow progress in FP in some countries: why? - Coverage >100%: measurement problem, reporting, population? - What to do if performance is high and plateau? - Is fee-for-service a right method for OP contact? 19
  20. 20. STRENGTHENING WORKS ON ADMINISTRATIVE DATA 1. Regularly monitoring program progress to identify candidates for adjustment (indicators and tools) 2. Taking advantage of HMIS data to compare with control facilities and assess performance on non-incentivized services 3. Developing online dashboard to facilitate use of data and promote transparency 4. Developing automated data analysis software to lessen burden of data analysis for teams and encourage focus on results (ADEPT RBF) 20
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