1. Drivers of Unit Cost Variation in Voluntary Medical Male Circumcision in Sub-Saharan Africa: A meta-regression analysis
Drew Cameron UC Berkeley
IAEN pre-conference
Amsterdam, Netherlands
20 July 2018

2. Overview
Goal: Use published VMMC costing data to create a predictive model to extrapolate to settings where no data exists
VMMC = Proof of Concept
Search and Screening
Extraction process
Data preparation
Exploration & analysis
Limitations & next steps

3. PRISMA for published HIV studies
Web of Science 2,682
Cochrane 810
PubMed 6,288
Embase 10,950
NHS EED 466
LILACS 163
Gray Lit & Google Scholar 2,399
Database Searching Total 21,539
Records identified 29
Records from other sources (Avenir, UCR, LSHTM, DCP3, snowball) 471
Unique Records (de-duped) 11,717
The following searches, validation, and snowball sampling activities were run between January , and October 2, 2016.
Records Excluded (non relevant topics, low yield hits) 9,199
HIV Articles Assessed for Eligibility 2,566
VMMC Studies Extracted 58
VMMC Studies Finally Included (modeled & duplicate data removed) 29

4. Data Extracted 58+ MC studies extracted (2016-7)
VMMC Studies Extracted 58
VMMC Studies Included (modeled & duplicate data removed) 29
58+ MC studies extracted (2016-7)
Early analysis & lessons learned
Validated by analyst
Quality assurance (early 2018)
Removed 29 studies (29 total)
Costs modeled or duplicative
Insufficient reporting (gray lit)

5. Unit of observation Average unit cost of VMMC procedure (per patient)
Country
Studies
Unit costs
No. sites
Botswana 1 2
Kenya 6
251
Lesotho 4
Mozambique
Namibia 7 49
Rwanda +2
South Africa 11 31
Swaziland
Tanzania 14
Uganda 5 10 65
Zambia 23 25
Zimbabwe 13
Total 29 89
503
Average unit cost of VMMC procedure (per patient)
often average of multiple facilities per unit cost
Weight by number of facilities per cost
2016 USD (WB GDP price deflator; exchange rates)

6. Geographic distribution of unit costs
Figure 2. Heatmap of mean unit cost by country
Country
Mean unit cost
Botswana
$125.15
Kenya
$44.79
Lesotho
$64.50
Mozambique
$32.31
Namibia
$56.73
Rwanda
$43.96
South Africa
$107.68
Swaziland
$61.79
Tanzania
$69.25
Uganda
$34.75
Zambia
$70.06
Zimbabwe
$61.71

7. Geographic distribution of unit costs
Figure 2. Heatmap of mean unit cost by country
Country
Mean unit cost
Botswana
$125.15
Kenya
$44.79
Lesotho
$64.50
Mozambique
$32.31
Namibia
$56.73
Rwanda
$43.96
South Africa
$107.68
Swaziland
$61.79
Tanzania
$69.25
Uganda
$34.75
Zambia
$70.06
Zimbabwe
$61.71

8. Data preparation Removed unit costs with poor reporting (n=48)
No input costs reported (n=38)
Personnel (direct service delivery; support) & traded goods (i.e. kits) not reported (n=10)
41 total unit costs remain with sufficient data

9. Unit cost variation by country

10. Analysis goals
Review: Use published unit cost estimates to predict VMMC costs in countries/settings/years with no evidence
Using mean and quantile regression
Use variables that are good predictors of unit cost variation (extract: urbanicity, facility type, ownership)
Additional data
Per capita GDP by country-year (World Bank)
Annual PEPFAR / Global Fund funding by country-year (IHME)
MC coverage by country (Morris et al. 2016)
HIV prevalence; ART coverage by country-year (World Bank)

11. Covariate missingness
Facility Type
Freq.
Percent
Clinic 3
7.3
Mixed (clinics & hospitals) 7
17.1
Hospitals 23
56.1
Mobile / community based 8
19.5
Ownership
Freq.
Percent
Public 21
51.2
Mixed (public & private) 5
12.2
Private / NGO 15
36.6
Urbanicity
Freq.
Percent
Urban / peri-urban 22
53.7
Mixed (urban & rural) 9
22.0
Rural 10
24.4

12. Covariate missingness
Facility Type
Freq.
Percent
Clinic 3
7.3
Mixed (clinics & hospitals) 7
17.1
Hospitals 23
56.1
Mobile / community based 8
19.5
Ownership
Freq.
Percent
Public 21
51.2
Mixed (public & private) 5
12.2
Private / NGO 15
36.6
Urbanicity
Freq.
Percent
Urban / peri-urban 22
53.7
Mixed (urban & rural) 9
22.0
Rural 10
24.4

13. Focus on cost inputs
Focused on predictive models of key inputs (personnel, supplies, residuals)
Start with predicting personnel costs

14. Model Selection
Attempted multiple models, functional forms, specification tests, robustness checks
OLS (log – log)
Country fixed effects
GLM
Random & mixed effects models
Quantile regression

15. GLM (link log; family gamma)
VARIABLE
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Key info
Year of data collection (2016 reference)
-0.397
-1.003
0.264
-3.317***
-3.047***
-3.743***
-4.402***
GDP per capita (mean centered)
0.011
0.012**
0.008***
0.009***
0.000
-0.002
GDP per capita^2 (mean centered)
-0.000
-0.000**
-0.000***
0.000**
Urbanicity (urban omitted)
Mixed
-8.912***
5.456***
6.817**
5.605***
6.513***
Rural
10.440*
5.107
2.839
2.483
2.531
Ownership (public omitted)
***
***
***
***
Private / NGO
-0.201
-0.061
19.981
14.648
Facility type (clinic omitted)
3.807
1.450
Hospital
2.168
-1.261
Mobile / community based
4.794
1.263
Health system
HIV prevalence of pop (age 15-49)
***
77.945
MC coverage of men (all ages)
-7.680
4.391
ART coverage of HIV+ pop (age 15-49)
31.928**
36.548**
Donor engagement
PEPFAR funding (per capita)
-0.467
Global Fund funding (per capita)
0.949
BIC
Observations 41
Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1

16. GLM (link log; family gamma)
VARIABLE
(1)
(2)
(3)
(4)
(5)
(6)
(7)
Key info
Year of data collection (2016 reference)
-0.397
-1.003
0.264
-3.317***
-3.047***
-3.743***
-4.402***
GDP per capita (mean centered)
0.011
0.012**
0.008***
0.009***
0.000
-0.002
GDP per capita^2 (mean centered)
-0.000
-0.000**
-0.000***
0.000**
Urbanicity (urban omitted)
Mixed
-8.912***
5.456***
6.817**
5.605***
6.513***
Rural
10.440*
5.107
2.839
2.483
2.531
Ownership (public omitted)
***
***
***
***
Private / NGO
-0.201
-0.061
19.981
14.648
Facility type (clinic omitted)
3.807
1.450
Hospital
2.168
-1.261
Mobile / community based
4.794
1.263
Health system
HIV prevalence of pop (age 15-49)
***
77.945
MC coverage of men (all ages)
-7.680
4.391
ART coverage of HIV+ pop (age 15-49)
31.928**
36.548**
Donor engagement
PEPFAR funding (per capita)
-0.467
Global Fund funding (per capita)
0.949
BIC
Observations 41
Standard errors in parentheses; *** p<0.01, ** p<0.05, * p<0.1

17. Conclusions Pro Good scope of countries included
Consistent relationships with
GDP Per Capita
Year
Con
VMMC = small sample size for analysis (n=41)
Not enough good reporting of other characteristics
Mean data masks variation in unit costs within countries/studies
Extracted MC data alone insufficient for prediction