1. Size Estimates of MSM: Ghana Men’s Study II
MAPPING AND POPULATION SIZE ESTIMATION (MPSE) AND INTEGRATED BIO-BEHAVIORAL SURVEILLANCE SURVEY (IBBSS) AMONG MEN WHO HAVE SEX WITH MEN (MSM) IN GHANA (II ROUND OF NATIONAL SURVEY, 2017)
NHARCON 2018

2. Size Estimates

3. Population Size Estimates Methods
Service multiplier
Unique Object
Literature
SS-PSE(RDS-A)
Mapping

4. Service Multiplier Two independent sources of data:
Source 1: HIV/AIDS services (e.g. HTS)
# of MSM accessing services[M]
Source 2: MSM survey (Indicates MSM at risk)
“In the past 6 months, have you received HTS?”
Obtain % or proportion of source 2 [P]
Needs to be representative of population
Population size (N) = M/P
Variance: V(N) = {Var(M)/[E(P)]2 }+{{[E(M)]2/[E(P)]4}*VAR(P)}
Standard error(se) = SQRT(V(N))
95% Confidence interval bound : point estimate +/-1.96(se)

5. Key assumptions: Service Multiplier
Two data sources are statistically independent
Survey population is representative of population of interest
Can result in over/under estimation
If Source 2 oversamples %MSM that get HTS the N will be lower than truth
If Source 1 HTS or service data quality:
low report of MSM may lead to underestimation
Double counting at HTS sites, repeat testing

6. Unique object Multiplier
Two independent and overlapping samples :
Source 1: “marked’ by distribution of unique objects( M)
Unique object- a locally made bracelet with a distinct pattern distributed to MSM in the geographic area of the full RDS survey
Source 2: representative sample of MSM from survey
“In the past 6 months, have you received a unique object (describe object)? “
Obtain % or proportion of source 2 [P]
Population size (N) = M/P
Variance: V(N) = {Var(M)/[E(P)]2 }+{{[E(M)]2/[E(P)]4}*VAR(P)}
Standard error(se) = SQRT(V(N))
95% Confidence interval bound : point estimate +/-1.96(se)
Things to consider
Does Matter
Accurate counts of the object given out to those eligible for the survey
Independence between who gets the object and who participates in the survey
Wide distribution of the object i.e. not clustered by “spots
Does not Matter
Random distribution of the object
# of objects given out relative to the size of the population or the sample size

7. Key assumptions: Unique Object Multiplier
Two data sources are statistically independent
Survey population is representative of population of interest
Thus,
Unique object multipliers afford the greatest control and potentially most accurate estimates of size
Problems with Unique object Multiplier
Inaccuracy in knowing # of respondents who received the object
Inaccuracy in measuring the % survey respondents who received object
Non representative sample
If actual # of objects to eligible people is smaller, the pop size is overestimated
If real # of objects is smaller then pop size is overestimated
If real % in pop is smaller than % in survey, then pop size is underestimated
If real % in pop is larger than % in survey, then pop size is Overestimate

8. Successive sampling -Population size estimate (SS-PSE)
SS-PSE uses a Bayesian framework, adopting prior knowledge or educated approximations of population size.
Assumes a posterior predictive distribution for population using data sources:
prior belief about population size.
Priors need not be precise, but provide a rough idea based on expert belief, estimates from other methods, literature review, etc.
Self-reported network size by participants’
Order of sampling of participants indicated by the recruitment date.

9. Successive sampling -Population size estimate (SS-PSE)
Rather than use self-report network size, we used “impute visibility” to avoid errors including
coarsening (heaping or rounding) of responses
under or over reporting by participant.
Imputed visibility =f(self-reported network size)
Estimated from information collected during the RDS study.
Has a measurement error model to account for errors in self-reported network size.

10. Literature Review
Focused on a synthesis of local demographic data, local and international data on prevalence of key populations.
Review of published and grey literature
relevant data from similar cities and regions
to calculate benchmark estimates
to estimate proportion in the male population ( Example Ghana Men's study I provide estimates of % adult males in Population in surveyed and non surveyed sites

11. Mapping Method Step 1: List of study regions /cities
Step 2: Visit regions /cities for ethnographic mapping of hotspots
Generate list of hotspots where MSM congregate or meet new sexual partners; estimate minimum and maximum MSM who frequent the hotspots; and a count of MSM found at hotspot
Participants
Secondary key informants (KIs) with information on locations or spots (‘‘hotspots’’). SKIs included people knowledgeable about their local area
from a variety of public places such as at taxi ranks, bus stops, fuel stations, shopping malls, streets, bars, and other workplaces. Also MSM peer educators or MSMs

12. ..cont. Size estimation: Mapping Method
Step 2: Outputs
A comprehensive list of unique spots MSM
MSM typologies of the spots (for example, bar, street, massage parlour, among others)
operational dynamics of each spot (for example, peak and non-peak times)
Estimated minimum and maximum number of MSM at hotspots, and actual counts of MSM found at site

13. ..cont. Mapping Method
Step 3: Validation of hotspots: re-visits of hotspots in step 2 to
Validate sites/spots from site owners or MSM on site; Ascertain the sites/spots were active or inactive (frequented by MSM)
obtain number of MSM (Minimum and Maximum);
Interview MSM on site to obtain data on frequency of visits (to adjust for double counting in size estimates )
Approach
Use MSM peer educators through existing MSM programmes in each selected region or city, where they were available, to accompany the study team to the identified spots and
mobilise individual MSM for interviews on estimated MSM population at the spots. (Take all approach i.e. interview all MSM on site).

14. ..cont. Size estimation: Mapping Method
Step 4: Size Estimation
Use Step 3 Data in generating population size estimates adjusted for duplication (using frequency of visits)
For spots not re-visited, use an average of the estimates from secondary key informants in Step 2

15. Consensus on estimates: Process
Round1: study team presented estimates and justifications for the estimates based on data generated from the various methods used
Round2 : participants/experts to discussed the initial estimates and also introduce new data outside the survey results, with appropriate justifications.
Round 3: established the best point estimate by region to be aggregated to the national size estimate.
Round 4: Participants agreed on a range (lower and upper bounds) that encompass the point estimate in the previous round.

16. RESULTS

17. Obtain service multiplier by adding numbers obtained from different services

19. d. Proportion receiving object
Unique Object multiplier Population Estimâtes
Region
c. Number of Objects
d. Proportion receiving object
StdErr
Population Size
95%CI Lower
95%CI Upper
Greater Accra
1896
0.2107
0.0213
8 999
7 171
10 828
Ashanti
1604
0.1299
0.0172
12 348
9 082
15 615
Brong-Ahafo
1901
0.3446
0.0240
5 517
4 724
6 311
Central
1347
0.4014
0.0268
3 356
2 883
3 830
Eastern
0.109
0.0145
17 394
12 809
21 982
Northern
568
0.4401
0.0278
1 291
1 100
1 483
Volta
2973
0.6245
0.0253
4 761
4 347
5 176
Western
0.1907
0.0210
8 411
6 555
10 270

20. RDS-A: Successive sampling -Population size estimate (SS-PSE)
RDS-A: Successive sampling -Population size estimate
(SS-PSE)
Region
Population Size
95%CI Lower
95%CI Upper
Greater Accra
5 220
4 140
5 595
Ashanti
4 919
3 915
5 227
Brong-Ahafo
4 894
3 731
5 294
Central
4 824
3 720
5 201
Eastern
4 872
3 841
5 213
Northern
5 012
4 004
5 315
Volta
4 983
3 932
5 310
Western
4 987
3 982
5 305

21. Literature based size estimation
Literature based size estimation
Region
e. Number of adult male (18+) (Source Projected pop 2016)
f. Percent MSM in adult male population (Source GMS I)
Population Size(e*f)
Greater Accra
0.51%
7 344
Ashanti
0.72%
10 522
Brong-Ahafo
689205
3 507
Central
622767
1.20%
7 472
Eastern
809433
2.31%
18 729
Northern
5 886
Volta
643635
3 275
Western
783835
9 404

22. Size estimate using 4 methods
Service multipliers
Unique Object multiplier
Literature
RDSAnalyst SS-PSE Method
Region
Size Estimate
95% CI Lower
95%CI Upper
95%CI Lower
Greater Accra
9,345
7,720
10,969
8,999
7,171
10,828
7,344
5,220
4,140
5,595
Ashanti
17,978
13,031
22,925
12,348
9,082
15,615
10,522
4,919
3,915
5,227
Brong-Ahafo
470
402
539
5,517
4,724
6,311
3,507
4,894
3,731
5,294
Central
716
630
803
3,356
2,883
3,830
7,472
4,824
3,720
5,201
Eastern
3,761
3,040
4,482
17,394
12,809
21,982
18,729
4,872
3,841
5,213
Northern
1,291
1,100
1,483
5,886
5,012
4,004
5,315
Volta
343
291
396
4,761
4,347
5,176
3,275
4,983
3,932
5,310
Western
1,407
1,244
1,569
8,411
6,555
10,270
9,404
4,987
3,982
5,305
34,018
62,076
66,139
39,711

23. MSM Population derived from a combination of all 4 methods
Region
Size Estimate
Lower Plausibility bound
Upper Plausibility bound
MSM as % of adult males
lower % of MSM to adult males
Upper% of MSM to adult males
Population of adult males 18+years
Greater Accra
8,171
5,220
9,345
0.57%
0.36%
0.65%
1,443,229
Ashanti
11,435
4,919
17,978
0.78%
0.33%
1.22%
1,471,247
Brong-Ahafo
4,200
470
5,517
0.61%
0.07%
0.80%
689,205
Central
4,090
716
7,472
0.66%
0.11%
1.20%
622,767
Eastern
11,133
3,761
18,729
1.38%
0.46%
2.31%
809,433
Northern
5,012
1,291
5,886
0.43%
0.51%
1,156,732
Volta
4,018
343
4,983
0.62%
0.05%
0.77%
643,635
Western
6,699
1,407
9,404
0.85%
0.18%
783,835
Overall Size
54,759
18,126
79,313
0.72%
0.24%
1.04%
7,620,083

24. MSM Population in Ghana

25. Collaborating Institutions
Research partners
Kwame Nkrumah University of Science and Technology (KNUST)
Human Sciences Research Council (HIV/AIDS, STIs and TB (HAST) research programme)
The Ghana AIDS Commission (GAC)
The Centers for Disease Control and Prevention (CDC)
Coordination
GAC
Funding

26. Acknowledgements Ghana Police Service
The Scientific Advisory Committee
We thank the study participants who consented and patiently went through the data collection process without whom we would not have had a GMS II.
The Maritime Life Precious Foundation (MARITIME), Ghana HIV/AIDS Network (GHANET), JSI, Centre for Popular Education and Human Rights, Ghana (CEPEHRG), MIHOSO, WAAF, WAPCAS, Kharis Foundation, Apt Sexual and Reproductive Health Foundation, 4H and the Community Development Alliance (CDA), Wa, for the immense support provided for community entry and mobilization. The support of the Komfo Anokye Teaching Hospital and Ghana Health Service Regional Hospital Laboratories is acknowledged.

27. Funders
This research has been supported by PEPFAR through the U.S. Centers for Disease Control and Prevention (CDC) under the terms of Cooperative Agreement - Supporting the GAC for Coordination of the Multi-Sectoral Response to HIV/AIDS in Ghana under PEPFAR, GH000088/03- 05