1. Sergio Bautista-Arredondo INSP
VMMC unit costs variation and determinants using facility-level, primary data from several studies.
Sergio Bautista-Arredondo
INSP

2. Motivation Objective Limitations of secondary data
To estimate determinants of unit cost variation and predict cost curves for different scenarios
Scale
Other service provision characteristics
Limitations of secondary data
Mean aggregated data not granular enough for analysis
Country variation but not enough variation (or reporting) of other characteristics
No single study could address this objective

3. Data Obtainment and Standardization

4. Data Obtainment and Standardization

5. Data Obtainment and Standardization

6. Description of data Observations 220 Urbanicity Rural Facilities 50%
Observations
220
Urbanicity
Rural Facilities
50%
Urban Facilities
Ownership
Private facilities
37%
Public facilities
63%
Facility type
Hospitals
47%
Clinics
53%
Mean Unit cost per VMMC in USD (mean) 66
Average VMMC per year
1097
Number of studies 7
Countries 8
Description of data

7. Primary data

8. Determinans of unit cost
We conducted OLS and GLM regression models to estimate the association between facilities’ characteristics and unit cost variation across sites
We identified the best model based on AIC and BIC criteria
We used this model to predict cost curves with respect to scale, for different implementation scenarios

9. Determinants of unit cost variation
OLS
GLM
Scale (log)
-0.137***
-0.150***
(0.031)
(0.035)
Scale (log2)
-0.007
-0.008
(0.014)
(0.015)
Rural=1, Urban=0
0.011
0.004
(0.079)
(0.090)
Private=1 , Public=0
0.275**
0.168
(0.125)
(0.135)
Hospital=1, Clinic=0
0.617***
0.629***
(0.111)
(0.119)
Outreach=1 , Fix=0
0.278**
0.296**
(0.121)
(0.136)
Year of data collection (2016 reference year)
0.146***
0.186***
(0.029)
Hospital*Private
-0.529***
-0.535***
Constant
4.131***
4.495***
Observations
220
Standard errors in parentheses *** p<0.01, ** p<0.05, *p<0.1

10. Determinants of unit cost variation (cont.)
OLS
GLM
GDP 2016 (log)
0.562***
0.492***
(0.062)
(0.067)
Constant
4.131***
4.495***
Observations
220
Standard errors in parentheses
*** p<0.01, ** p<0.05, *p<0.1

11. Scenarios (example) Country Urbanicity Ownership Type Facility Type
Observed Unit cost per VMMC
Kenya
Urban
Public
Clinic
53.6
Hospital
37.5
Private
34.4
45.1
Rural
60.4
28.5
39.8
43.3
Namibia
87.8
South Africa
75.7
170.6

12. Produce scenarios

13. Cost curves per country

14. Summary
We identified studies on VMMC unit costs and gathered data from 7 of them
We are in the process of obtaining and standardizing data on ART, HTC, PMTCT, services for KP – about 1,600 observations so far
We identified facility-level characteristics associated with unit cost variations
Based on coefficients obtained from multivariate regression models we modeled cost curves for implementation scenarios

15. Thank you sbautista@insp.mx

16. Validate scenarios: South Africa
Fix effects model
GLM
Random effects model

17. Validate scenarios: Kenya
Fix effects model
GLM
Random effects model

18. Quantile regressions:
Assess whether determinants influence unit costs differently in different quantiles of unit costs
Mean
p10
p25
p50
p75
p90
Rural
-0.017
0.083
-0.011
-0.006
0.009
0.026
Private
0.291**
0.318*
0.255*
0.238*
0.235
0.167
Hospital
0.454***
0.444***
0.299**
0.275**
0.398***
0.453*
Outreach
0.040
0.091
0.034
0.072
-0.089
0.163
Scale (log)
-0.238***
-0.228***
-0.216***
-0.233***
-0.227***
-0.186**
Scale (log) 2
-0.026*
-0.049**
-0.035*
-0.019
-0.021
-0.034
Year of collection
(2016 ref.)
0.101**
-0.095
0.019
0.104**
0.162***
0.199**
Hospital*Private
-0.291*
-0.316
-0.160
-0.087
-0.237
-0.346
Constant
4.013***
2.388***
3.319***
3.998***
4.646***
5.004***
Observations
220

19. Cost curves per country
74%
100%
85%
Note: % represents the percentage of observerved points contained by the scenarios
94%
70%
27%
55%
88%