1. Ali Mirzazadeh MD. MPH. PhD.
9/20/2013
Network Scale-Up to Estimate the Population Size of High-Risk Groups for HIV
Methods Core Seminars – Center for AIDS Prevention Studies/UCSF – 20 Sep. 2013
Ali Mirzazadeh MD. MPH. PhD.
Institute for Health Policy Studies / Global Health Sciences Institute UCSF, San Francisco, CA, USA
Regional Knowledge Hub, and WHO Collaborating Center for HIV Surveillance, Kerman University of Medical Sciences, Kerman, Iran
NSU Workshop (Overview PSE - AM)

2. This presentation has the following parts
P1: PSE methods overview
P2: Network Scale-up method overview
P3: Network size estimation
P4: Correction for biases in NSU

3. Part 1
PSE methods overview

4. Why do MARP size estimates?
Know, track, and predict your epidemic
Disproportionate impact in low level, concentrated, and generalized epidemics
Program planning
Advocacy, development, M&E
Because you were asked to
UNAIDS, UNGASS, PEPFAR, MOH
Resource allocation
Right population, right priority, right amount on right programs

5. How to do MARP size estimates?
There is no gold standard, no census
We do not know which method is best
We are not able to fully calibrate or correct
Many methods
Scientific rigor
Cost

6. Done in surveys of the general population
Census
Population-based survey
Network scale up
Oil wells
Done with surveys of MARPs
Multiple sample recapture
Capture-recapture
Plant recapture
Unique object multiplier
Truncated Poisson
Scientific rigor
Multipliers, multiple multipliers
Unique event multiplier
Mapping with census and enumeration
Nomination counting
Place, RAP, ethnography
Registries, police, SHC, drug treatment, unions, workplace
Wisdom of the crowds
Delphi
Consensus
Done by literature review, experts, stakeholders, models
Discrepancies
Soft modeling
Borrow from thy neighbor
Conventional Wisdom
Straw man
Cost

7. Done with surveys of MARPs
Direct Methods
Done with surveys of MARPs

8. Direct questions to population-based surveys
Strengths
Weaknesses
Surveys are familiar
Easy if a survey is underway
Straightforward to analyze
Sampling is easy to defend scientifically (“gold standard”)
Low precision when the behaviors are rare
Respondents are unlikely to admit to stigmatized behaviors
Only reaches people residing in households
Privacy, confidentiality, risk to subjects
Mirzazadeh A, Haghdoost AA, Nedjat S, Navadeh S, McFarland W, Mohammad K. Accuracy of HIV-Related Risk Behaviors Reported by Female Sex Workers, Iran: A Method to Quantify Measurement Bias in Marginalized Populations. AIDS Behav Feb;17(2):623-31

9. Census and enumeration
Ali Mirzazadeh, Faran Emmanuel, Fouzia Gharamah, Abdul Hamed Al-Suhaibi, Hamidreza Setayesh, Willi McFarland, Ali Akbar Haghdoost; HIV prevalence and related risk behaviors in men who have sex with men, Yemen 2011; AIDS Behav. 2013 Jul 23. [Epub ahead of print]

10. Census and enumeration
Strengths
Weaknesses
It is a real count, not an estimate
Can produce credible lower limit
Can be used to inform other methods
Use in program planning, implementation, evaluation
At-risk populations hidden, methods miss some members (China: multiply by 2 – 3!)
Stigma may cause members to not identify themselves
Time-consuming and expensive
Staff safety
Subject safety

11. Capture-recapture

12. Capture-recapture Strengths Weaknesses Relatively easy
Does not require much data
When no other data or studies are available
4 conditions hard to meet:
two samples must be independent , not correlated
each population member should have equal chance of selection
each member must be correctly identified as ‘capture’ or ‘recapture’
no major in/out migration

13. Nomination method
S Navadeh, A Mirzazadeh, L Mousavi, AA Haghdoost, N Fahimfar, A Sedaghat; HIV, HSV2 and Syphilis Prevalence in Female Sex Workers in Kerman, South-East Iran; Using Respondent-Driven Sampling Iran J Public Health. 2012 Dec 1;41(12):60-5. Print 2012.

14. Nomination method Strengths Weaknesses Relatively easy
Snowball or chain sampling methods
Need the target group to be connected/network
Time consuming
Broken chains
Biased to visible and accessible part of a target population (new statistical methods coming)
Promise to provide services

15. Multiplier methods STI Clinic
Johnston LG, Prybylski D, Raymond HF, Mirzazadeh A, Manopaiboon C, McFarland W. Incorporating the Service Multiplier Method in Respondent-Driven Sampling Surveys to Estimate the Size of Hidden and Hard-to-Reach Populations: Case Studies From Around the World Sex Transm Dis. 2013 Apr;40(4):304-10

16. Multiplier methods Strengths Weaknesses Uses available data sources
Flexible in sampling methods
When already doing an IBBSS
Two sources of data must be independent
Data sources must define population in the same way
Time periods, age, geographic areas must align
Inaccuracy of program data and survey data

17. Done in surveys of the general population
Indirect Methods
Done in surveys of the general population

18. Proxy respondent method
Proxy Respondent (Alter)
Respondent
Member of
Hidden Pop.
Mirzazadeh A, Danesh A, Haghdoost AA. Network scale-up and proxy respondent methods in prisons [ongoing]

19. Proxy respondent method
Strengths
Weaknesses
Estimates from general population rather than hard-to-reach populations
Doesn’t require directly asking sensitive questions or lengthy behavioral survey
Some subgroups may not associate with members of the general population
Respondent may be unaware the alter engages in the behavior of interest
Biases may arise by types of questions asked

20. Network scale-up
Shokoohi M, Baneshi MR, Haghdoost AA. Size Estimation of Groups at High Risk of HIV/AIDS
using Network Scale Up in Kerman, Iran. Int J Prev Med Jul;3(7):471-6.

21. Network scale-up Strengths Weaknesses
Estimates from general population rather than hard-to-reach populations
Doesn’t require directly asking sensitive questions or lengthy behavioral survey
Average personal network size difficult to estimate
Some subgroups may not associate with members of the general population
Respondent may be unaware someone in network engages in the behavior of interest
Biases may arise by types of questions asked

22. Part 2
NSU method overview

23. NSU Basic Concepts
A random sample of the general population describes their social networks
network sizes (C)
the presence of individuals belonging to special sub-populations of interest
Based on the prevalence and presence of sub-populations in the social network of the selected sample, the sizes of the hidden sub-populations in a community are estimated.

24. NSU – Main Questions
How many people do you know over the past two years?
Of those, how many injected drug (over the past two years)?
Do you know at least one person in your network who injected drug (over the past two years)?

25. NSU – Frequency Approach
T= total population with the size of t
C= one individual’s acquaintances (or personal network size)
m = the number of individuals belonged to the target population among those acquaintances
E = the hidden population with the size of e

26. NSU – Frequency Approach

27. NSU – Probability Approach
Frequency Approach

28. Confidence Interval 95% - Conventional
Frequency approach:
Point Estate E = P x t
95%CI Upper Limit E = (P se) x t
95%CI Lower Limit E = (P se) x t

29. Confidence Interval - Bootstrap

30. Size Estimation of Groups at High Risk of HIV/AIDS using Network Scale Up in Kerman, Iran
Age Male
Shokoohi M, Baneshi MR, Haghdoost AA. Size Estimation of Groups at High Risk of HIV/AIDS
using Network Scale Up in Kerman, Iran. Int J Prev Med Jul;3(7):471-6.

31. Seems to be easy / but challenging
What do you mean by ‘know’?
Subgroups (Sex, Age Groups, Local/National)?
How to define MARPS (IDU, FSW, MSM)?

32. Example – Data Collection Tool

33. Network size estimation
Part 3
Network size estimation

34. Definition of social network
9/20/2013
Definition of social network
Global network
Active network
Supportive network
Sexual network
Sub-networks
Family
Coworkers
Classmates
Sport …
NSU Workshop (Network Size Estimation - AAH)

35. Definition of “know”
People whom you know and who know you, in appearance or by name, with whom you can interact, if needed.
AND
With whom you have contacted over the last two years in person, or by telephone or
Living in your area/country
………..

36. Direct methods Overall question? How many do you know?
9/20/2013
Direct methods
Overall question? How many do you know?
Active network
Supporting network
Sub-networks
Sub-groups (summation method)
Family
Coworkers
Sports
Ex-classmates
Clubs
Church
……..
NSU Workshop (Network Size Estimation - AAH)

37. Disadvantages of direct methods
9/20/2013
Disadvantages of direct methods
Reliability and validity issue
Double counting in summation method
NSU Workshop (Network Size Estimation - AAH)

38. 9/20/2013
Indirect methods
C is estimated based on the frequency of members belonging to a sub-populations with known sizes (reference groups):
Number of birth in last year
Number of death due to cancer/car accident in last year
Number of marriage in last year
Number of people with specific first name
It is a type of back calculation
NSU Workshop (Network Size Estimation - AAH)

39. 9/20/2013
C – Network Size
NSU Workshop (Network Size Estimation - AAH)

40. Specific criteria for reference groups
9/20/2013
Specific criteria for reference groups
Prevalence between 0.1-4%
one-syllable name
Stable prevalence over time and in different ethnicities
NSU Workshop (Network Size Estimation - AAH)

41. Back calculation of the size of reference groups
9/20/2013
Back calculation of the size of reference groups
At least 20 reference groups are needed in the first step
Some of these reference group may generate bias estimates
Step by step, non-eligible reference groups has to be detected and dropped form the calculation:
Ratio Method
Regression Method
NSU Workshop (Network Size Estimation - AAH)

42. Ratio method algorithm
Step 1: including all reference groups, calculate C
Step 2: back-calculate the size of all reference groups (given C)
Step 3: calculate bias ratio [(Real size/Estimated size)–1] for every reference group
Step 4: exclude the most biased reference group, and recalculate C
Step 5: back-calculate the size of all remaining reference groups (given new C)
Step 6: recalculate bias ratio for every reference group
Step 7: check if all bias ratios are between 0.5 and 1.5
Step 8: if not, got to step 4 and continue till all bias ratios fall between 0.5 and 1.5

43. Calculate the network size
Computer Lab 1
Calculate the network size
C_estimation(withoutsolution).xml

44. Real vs. predicted size for 23 Ref. groups – Kerman NSU

45. Ratio Method – Kerman NSU
# Steps C
Min-Ratio
Max-Ratio
Removing group
Step 1
268.6
Step 2
288.8
0.06
2.57 m8
Step 3
333.1
0.07
2.61 m1
Step 4
366
0.08
1.96 m7
Step 5
414.7
0.09
1.91 m5
Step 6
383.6
0.23
1.76
m12
Step 7
379.2
0.25
1.74
m21
Step 8
371
0.27
1.71
m10
Step 9
365.9
0.29
1.68
m19
Step 10
360.2
0.41
1.66
m11
Step 11
382.5
0.44
1.57
m16
Step 12
398.9
0.46
1.54 m9
Step 13
386.1
1.49
m15
Step 14
372.2
0.49
1.43
m20
Step 15
365.5
0.57
1.41
m14

46. Ratio Method – Kerman NSU
Plot real versus predicted size of reference groups
variable
Real
Estimate
Ratio m2
478423
1.17 m3
610018
1.14 m4
252786
0.73 m6
137200
1.33
m13
206942
0.75
m17
119784
1.05
m18
249592
1.41
m22
81321
0.57
m23
73800
0.71

47. Regression Method
NSU assumes a linear association between prevalence of reference groups in the society (e/t) and average number of people respondents knew in each reference group (Average of m)
To detect reference groups that does not satisfy the linearity assumption, fit a regression line and calculate standardize DFBETA for all reference groups.
The reference group with the highest SDFBETA is excluded.
The process is continued in an iterative fashion to remove all reference groups with SDFBETA higher than 3/√n (n is the number of reference groups)

48. Regression Method – Iran NSU

49. Regression Method – Iran NSU
STATA commands:
reg meanm propm, beta
dfbeta
disp 3/sqrt(23)
id propm meanm _dfbeta_1 m m m m m m m m m m m m m m m m m m m m m m m

50. Real vs. Predicted Size Ratio and Regression Methods
Final Network Size
Ratio M. 380
Regression M. 308
Glob J Health Sci. 2013 Jun 17;5(4): doi: /gjhs.v5n4p217.
The estimation of active social network size of the Iranian population.
Rastegari A, Haji-Maghsoudi S, Haghdoost A, Shatti M, Tarjoman T, Baneshi MR.

51. Correction for biases in NSU
Part 4
Correction for biases in NSU

52. Main Biases in NSU
Transmission effect: a respondent may be unaware someone in his/her network engages in the behavior of interest.
Barrier effects: some subgroups may not associate with members of the general population.

53. NSU adjustment factors (1)
9/20/2013
NSU adjustment factors (1)
Transparency (also known as visibility ratio, transmission error, transparency rate, transmission rate, and masking)
Respondents may know people who are drug users, but might not know if they inject drug, a phenomenon called information transmission error
-> Failure to adjust for it may lead to an underestimate of unknown size
NSU Workshop (Plausibility And Correction Factors - AAH)

54. NSU adjustment factors (2)
9/20/2013
NSU adjustment factors (2)
Barrier Effect (Also known as Popularity ratio, Degree ratio)
People with high-risk behaviors might, on average, have smaller networks than the general population making them less likely to be counted by individuals reporting on people they know.
-> Failure to adjust for it may lead to an underestimate of unknown size
NSU Workshop (Plausibility And Correction Factors - AAH)

55. NSU adjustment factors (3)
9/20/2013
NSU adjustment factors (3)
Social Desirability Bias (also know as response bias)
Respondents may know people who are for example sex worker, but may be unwilling to provide this information because of the possible stigma involved.
-> Failure to adjust for it may lead to an underestimate of unknown size
Mirzazadeh A, Haghdoost AA, Nedjat S, Navadeh S, McFarland W, Mohammad K. Accuracy of HIV-Related Risk Behaviors Reported by Female Sex Workers, Iran: A Method to Quantify Measurement Bias in Marginalized Populations. AIDS Behav Feb;17(2):623-31
NSU Workshop (Plausibility And Correction Factors - AAH)

56. Game of contacts: transmission rate
9/20/2013
Game of contacts: transmission rate
In a sample of target high-risk population, let’s say 300 IDU, we ask the number of people they know with A, B etc. name.
And how many of them (i.e. those people named A, B…) know about their behavior (e.g. injecting drug).
The transmission rate is estimated by dividing the summation of the number of alters of respondents that are aware of their behavior by the total number of alters.
NSU Workshop (Plausibility And Correction Factors - AAH)

57. Game of contacts: transmission rate
9/20/2013
Game of contacts: transmission rate
NSU Workshop (Plausibility And Correction Factors - AAH)

58. Game of contacts: popularity ratio
9/20/2013
Game of contacts: popularity ratio
The game of contacts estimates the relative personal network size of members of the high-risk population and the general population
Selecting a list of first names
Asking from a sample of the general population how many people they know with one of the selection first names
Asking from a sample of the target population how many people they know with one of the selection first names
NSU Workshop (Plausibility And Correction Factors - AAH)

59. Game of contacts: popularity ratio
9/20/2013
Game of contacts: popularity ratio
NSU Workshop (Plausibility And Correction Factors - AAH)

60. Visibility and Popularity Factors – NSU Iran
VF for
IDU: 54% (95% UL: 50%, 58%)
FSW: 44% (95% UL: 41%, 49%)
PF for
IDU: 69% (95% CI: 59%, 80%)
FSW: 74% (95% CI: 68%, 81%)

61. Visibility and Popularity Factors – NSU Iran
Sample size = people (400 per province)
Total Pop size = 75,149,699
Iran NSU
Pop. Size Estimates
(Point Estimate)
(95% CI)
Prevalence (95% CI)
Alcohol
1,300,858
(1,195, ,426,513)
1.73 ( )
Opium
1,101,411
(973, ,273,240)
1.47 ( )
Opium sap (Shireh)
493,156
(437, ,938)
0.66 ( )
Amphetamine, ecstasy and LCD
224,357
(205, ,362)
0.30 ( )
Cristal
439,861
(387, ,428)
0.59 ( )
Heroin / Crack
262,344
(235, ,184)
0.35 ( )
Marijuana / Hashish
352,592
(311, ,857)
0.47 ( )
Any drug injection
207,722
(182, ,363)
0.28 ( )

62. Validation Study: Social Desirability Bias
Mirzazadeh A, Haghdoost AA, Nedjat S, Navadeh S, McFarland W, Mohammad K. Accuracy of HIV-Related Risk Behaviors Reported by Female Sex Workers, Iran: A Method to Quantify Measurement Bias in Marginalized Populations. AIDS Behav Feb;17(2):623-31
Mirzazadeh A, Mansournia MA, Nedjat S, Navadeh S, McFarland W, Haghdoost AA, Mohammad K; Bias analysis to improve monitoring an HIV epidemic and its response: approach and application to a survey of female sex workers in Iran; J Epidemiol Community Health 2013;67:10   Published Online First: 27 June 2013

63. Key Resources
Rastegari A, Haji-Maghsoudi S, Haghdoost A, Shatti M, Tarjoman T, Baneshi MR. The estimation of active social network size of the Iranian population. Glob J Health Sci. 2013 Jun 17;5(4):217-27
Shokoohi M, Baneshi MR, Haghdoost AA. Size Estimation of Groups at High Risk of HIV/AIDS using Network Scale Up in Kerman, Iran. Int J Prev Med Jul;3(7):471-6.
Bernard HR, Hallett T, Iovita A, Johnsen EC, Lyerla R, McCarty C, Mahy M, Salganik MJ, Saliuk T, Scutelniciuc O, Shelley GA, Sirinirund P, Weir S, Stroup DF; Counting hard-to-count populations: the network scale-up method for public health; Sex Transm Infect Dec;86 Suppl 2:ii11-5.
(publications)

64. Thank You So Much