1. Filling in the gaps: Extrapolations of VMMC unit costs and economies of scale using pool data from several studies.
Carlos Pineda

2. Limitations of primary data
Strengths of primary data:
Identify facility characteristics with strongest cost driving effects
Generate unit cost scenarios across varying levels of scale
Challenge:
Predict unit costs for countries outside of our sample

3. Objective
Utilize existing research on VMMC services in Africa to create cost prediction models to project the costs of VMMC in a variety of settings in Sub-Saharan Africa.
Approach
Collapse primary and secondary data
Multivariate regression (GLM and OLS) to extrapolate countries outside our sample
Validate models by removing data and predicting costs

4. Data Obtainment and Standardization

5. Data Obtainment and Standardization

6. Process of collapsing

7. Collapsed Secondary Data
Description of Data
Collapsed
Primary Data
Collapsed Secondary Data
Pooled
Data
Observations 38 9 47
Urbanicity
Rural Facilities (%) 50
22.2
44.7
Urban Facilities (%)
77.8
55.3
Ownership
Private facilities (%)
36.8
33.3
36.2
Public facilities (%)
63.2
66.6
63.8
Facility type
Hospitals (%)
52.6
Clinics (%)
47.4
Mean unit cost per VMMC (USD) 64 69 65
Average number of VMMC per year
1212 --
Average VMMC coverage in 2016 (%) 44 30 41
Number of studies 7 16
Average number of facilities per observation 6
5.2 5
Countries 8 10

8. Extrapolation Model -Variables at facility level (characteristics)
OLS
GLM
Variables
Unit cost
Hospital=1, Clinic=0
0.156
0.118
(0.127)
(0.138)
log_GDP2016
0.494***
0.466***
(0.084)
(0.086)
Private=1 , Public=0
0.152
0.107
(0.134)
(0.142)
Rural=1, Urban=0
0.102
0.098
(0.124)
(0.136)
Health Sector Salary Index
2.558***
2.858***
(0.567)
(0.595)
VMMC coverage
-0.584
-0.903
(1.085)
(1.130)
VMMC coverage 2
0.666
0.959
(1.024)
(1.092)
Constant
-0.327
-0.258
(0.507)
(0.593)
Observations (Unit Costs Scenarios) 47
R-squared
0.664
Standard errors in parentheses
*** p<0.01, ** p<0.05, * p<0.1
-Variables at facility level (characteristics)
-Variables at country level
(GDP, coverage and prevalence)
Two criteria:
Goodness of fit
Performance of predictions

9. Results of Extrapolations
Validate model:
Remove countries from sample, iteratively
Estimate parameters
Extrapolate unit costs

10. Results of Extrapolations
Validate model:
Remove countries from sample, iteratively
Estimate parameters
Extrapolate unit costs

11. Validation of Extrapolations

12. Percentage Error

13. At this point we are able to respond the question “How much does it cost?”
But we wanted to do more...

14. Reminder: Cost Curves Per Scenario

15. Results of Extrapolations

16. Combination of Point Estimates and Cost Curves
The estimation point comes from extrapolations

17. Combination of point estimations and cost curves
The cost curves represent a predicted unit cost scenario with scale effects

18. Combination of point estimations and cost curves
The cost curves represent a predicted unit cost scenario with scale effects

19. Combination of Point Estimates and Cost Curves
The cost curves represent a predicted unit cost scenario with scale effects

20. Conclusions:
We performed VMMC unit cost prediction models, based on primary and secondary data obtained from ten African countries.
We validated our predictions by systematically removing data from one country from our sample, predicting unit costs for that country and comparing them to empirical data.
We combined point estimations with estimated unit cost curves for different scenarios (facility characteristics).