network scale-up to estimate the population size of high-risk groups for hiv
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Network Scale-Up to Estimate the Population Size of High-Risk Groups for HIV
Ali Mirzazadeh MD. MPH. PhD.
Institute for Health Policy Studies / Global Health Sciences InstituteUCSF, San Francisco, CA, USA [ali.mirzazadeh@ucsf.edu]
Regional Knowledge Hub, and WHO Collaborating Center for HIV Surveillance, Kerman University of Medical Sciences, Kerman, Iran [ali.mirzazadeh@hivhub.ir]
Methods Core Seminars – Center for AIDS Prevention Studies/UCSF – 20 Sep. 2013
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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
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Part 1
PSE methods overview
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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
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How to do MARP size estimates?
Cost
Scie
ntific
rigo
r
• 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
6Cost
Scie
ntific
rigo
r
Straw manConventional Wisdom
Borrow from thy neighborSoft modeling
Consensus
Wisdom of the crowdsDelphi
Registries, police, SHC, drug treatment, unions, workplace
Discrepancies
Place, RAP, ethnography
Unique event multiplier
Truncated PoissonMultipliers, multiple multipliers
Multiple sample recaptureCapture-recapture
Network scale upPopulation-based survey
Census
Nomination counting
Unique object multiplier
Mapping with census and enumeration
Plant recapture
Done with surveys of MARPs
Done by literature review, experts, stakeholders, models
Done in surveys of the general population
Oil wells
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Direct MethodsDone with surveys of MARPs
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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. 2013 Feb;17(2):623-31
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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]
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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
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Capture-recapture
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Capture-recapture
Strengths Weaknesses
• Relatively easy• Does not require much data • When no other data or studies are available
• 4 conditions hard to meet: 1) two samples must be
independent , not correlated2) each population member should
have equal chance of selection3) each member must be correctly
identified as ‘capture’ or ‘recapture’
4) no major in/out migration
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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.
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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
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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
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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
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Indirect MethodsDone in surveys of the general population
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Proxy respondent method
Member ofHidden Pop.
Proxy Respondent (Alter)
Respondent
Mirzazadeh A, Danesh A, Haghdoost AA. Network scale-up and proxy respondent methods in prisons [ongoing]
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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
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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. 2012 Jul;3(7):471-6.
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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
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Part 2
NSU method overview
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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.
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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)?
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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
cm
te
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NSU – Frequency Approach
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NSU – Probability Approach
Probability Approach Frequency Approach
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Confidence Interval 95% - Conventional
• Frequency approach:
• Point Estate E = P x t• 95%CI Upper Limit E = (P + 1.96 se) x t• 95%CI Lower Limit E = (P - 1.96 se) x t
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Confidence Interval - Bootstrap
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Size Estimation of Groups at High Risk of HIV/AIDS using Network Scale Up in Kerman, Iran
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. 2012 Jul;3(7):471-6.
KermanT = 132,651Age 15-45 Male
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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)?
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Example – Data Collection Tool
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Part 3
Network size estimation
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Definition of social network
• Global network• Active network• Supportive network• Sexual network• Sub-networks
– Family– Coworkers– Classmates– Sport– …
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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 e-mail AND • Living in your area/countryAND• ………..
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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– ……..
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Disadvantages of direct methods
• Reliability and validity issue• Double counting in summation method
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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
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C – Network Size
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Specific criteria for reference groups
• Prevalence between 0.1-4%• one-syllable name• Stable prevalence over time and in different
ethnicities
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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
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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
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Computer Lab 1
Calculate the network size
C_estimation(withoutsolution).xml
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Real vs. predicted size for 23 Ref. groups – Kerman NSU
0 200000 400000 600000 800000 1000000 1200000 14000000
100000
200000
300000
400000
500000
600000
700000
800000
f(x) = 0.402517342478602 x + 167391.806408975R² = 0.46920386995512
Real Size
Pred
icte
d Si
ze
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Ratio Method – Kerman NSU
# Steps C Min-Ratio Max-Ratio Removing group
Step 1 268.6Step 2 288.8 0.06 2.57 m8Step 3 333.1 0.07 2.61 m1Step 4 366 0.08 1.96 m7Step 5 414.7 0.09 1.91 m5Step 6 383.6 0.23 1.76 m12Step 7 379.2 0.25 1.74 m21Step 8 371 0.27 1.71 m10Step 9 365.9 0.29 1.68 m19
Step 10 360.2 0.41 1.66 m11Step 11 382.5 0.44 1.57 m16Step 12 398.9 0.46 1.54 m9Step 13 386.1 0.44 1.49 m15Step 14 372.2 0.49 1.43 m20Step 15 365.5 0.57 1.41 m14
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Ratio Method – Kerman NSU
Plot real versus predicted size of reference groups
0 100000 200000 300000 400000 500000 600000 7000000
100000
200000
300000
400000
500000
600000
700000
Real
Estim
ate
variable Real Estimate Ratio
m2 478423 409956.1 1.17
m3 610018 535452.8 1.14
m4 252786 347730.6 0.73
m6 137200 103534.8 1.33
m13 206942 274524.2 0.75
m17 119784 113992.9 1.05
m18 249592 177264.2 1.41
m22 81321 143798.4 0.57
m23 73800 103534.8 0.71
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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)
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Regression Method – Iran NSU
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Regression Method – Iran NSU
• STATA commands:reg meanm propm, betadfbetadisp 3/sqrt(23)
id propm meanm _dfbeta_1m1 .015655 1.75703 .9383446m2 .006402 2.00512 -.1981893m3 .008163 2.61893 -.6207414m4 .003383 1.70077 -.2671462m5 .011498 2.1509 .3982361m6 .001836 .506394 -.014008m7 .008614 1.09974 .0457343m8 .008182 .624041 -.2765261m9 .003746 .910486 -.0042227m10 .000292 .450128 .062993m11 .000259 .329923 .0412282m12 .000311 1.43478 -.2617438m13 .002769 1.34271 -.0880711m14 .000497 .381074 .0366919m15 .000839 .731458 .050291m16 .005542 1.2046 .0195055m17 .001603 .557545 .009407m18 .00334 .867008 -.0029265m19 .000204 .283887 .0327013m20 .000866 .754476 .0480117m21 .000148 .242967 .0244004m22 .001088 .703325 .043369m23 .000988 .506394 .0317765
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Real vs. Predicted SizeRatio and Regression Methods
0 100000 200000 300000 400000 500000 600000 7000000
100000
200000
300000
400000
500000
600000
700000RegresionLinear (Re-gresion)RatioLinear (Ratio)
Real
Estim
ate
Final Network SizeRatio M. 380Regression M. 308
Glob J Health Sci. 2013 Jun 17;5(4):217-27. doi: 10.5539/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.
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Part 4
Correction for biases in NSU
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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.
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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
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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
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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. 2013 Feb;17(2):623-31
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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.
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Game of contacts: transmission rate
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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
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Game of contacts: popularity ratio
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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%)
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Visibility and Popularity Factors – NSU Iran
Iran NSU Pop. Size Estimates
(Point Estimate)
Pop. Size Estimates
(95% CI)Prevalence (95% CI)
Alcohol 1,300,858 (1,195,530 - 1,426,513) 1.73 (1.59 - 1.90)Opium 1,101,411 (973,129 - 1,273,240) 1.47 (1.29 - 1.69)Opium sap (Shireh) 493,156 (437,521 - 565,938) 0.66 (0.58 - 0.75)
Amphetamine, ecstasy and LCD 224,357 (205,823 - 247,362) 0.30 (0.27 - 0.33)Cristal 439,861 (387,124 - 502,428) 0.59 (0.52 - 0.67)
Heroin / Crack 262,344 (235,188 - 296,184) 0.35 (0.31 - 0.39)
Marijuana / Hashish 352,592 (311,572 - 402,857) 0.47 (0.41 - 0.54)
Any drug injection 207,722 (182,671 - 238,363) 0.28 (0.24 - 0.32)
Sample size = 12814 people (400 per province)Total Pop size = 75,149,699
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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. 2013 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;67:10 882-887 Published Online First: 27 June 2013
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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. 2012 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. 2010 Dec;86 Suppl 2:ii11-5.
• www.hivhub.ir (publications)
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Thank You So Much
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