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 [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] NSU Workshop (Overview PSE - AM)

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

Part 1 PSE methods overview

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

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

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

Done with surveys of MARPs Direct Methods Done with surveys of MARPs

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

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]

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

Capture-recapture

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

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.

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

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

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

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

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]

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

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.

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

Part 2 NSU method overview

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.

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)?

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

NSU – Frequency Approach

NSU – Probability Approach Frequency Approach

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

Confidence Interval - Bootstrap

Size Estimation of Groups at High Risk of HIV/AIDS using Network Scale Up in Kerman, Iran Age 15-45 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. 2012 Jul;3(7):471-6.

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)?

Example – Data Collection Tool

Network size estimation Part 3 Network size estimation

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)

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 Living in your area/country ………..

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)

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)

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)

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

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)

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)

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

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

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

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

Ratio Method – Kerman NSU Plot real versus predicted size of reference groups 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 0.71

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)

Regression Method – Iran NSU

Regression Method – Iran NSU STATA commands: reg meanm propm, beta dfbeta disp 3/sqrt(23) id propm meanm _dfbeta_1 m1 .015655 1.75703 .9383446 m2 .006402 2.00512 -.1981893 m3 .008163 2.61893 -.6207414 m4 .003383 1.70077 -.2671462 m5 .011498 2.1509 .3982361 m6 .001836 .506394 -.014008 m7 .008614 1.09974 .0457343 m8 .008182 .624041 -.2765261 m9 .003746 .910486 -.0042227 m10 .000292 .450128 .062993 m11 .000259 .329923 .0412282 m12 .000311 1.43478 -.2617438 m13 .002769 1.34271 -.0880711 m14 .000497 .381074 .0366919 m15 .000839 .731458 .050291 m16 .005542 1.2046 .0195055 m17 .001603 .557545 .009407 m18 .00334 .867008 -.0029265 m19 .000204 .283887 .0327013 m20 .000866 .754476 .0480117 m21 .000148 .242967 .0244004 m22 .001088 .703325 .043369 m23 .000988 .506394 .0317765

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):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.

Correction for biases in NSU Part 4 Correction for biases in NSU

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.

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)

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)

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. 2013 Feb;17(2):623-31 NSU Workshop (Plausibility And Correction Factors - AAH)

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)

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

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)

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

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%)

Visibility and Popularity Factors – NSU Iran Sample size = 12814 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 (1,195,530 - 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)

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

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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