network scale-up to estimate the population size of high-risk groups for hiv

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 [[email protected]]

Regional Knowledge Hub, and WHO Collaborating Center for HIV Surveillance, Kerman University of Medical Sciences, Kerman, Iran [[email protected]]

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]

http://www.ncbi.nlm.nih.gov/pubmed/23877790



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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


• 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.

http://www.ncbi.nlm.nih.gov/pubmed/?term=HIV,+HSV2+and+Syphilis+Prevalence+in+Female+Sex+Workers+in+Kerman,+South-East+Iran;+Using+Respondent-Driven+Sampling

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Nomination method


• 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

http://www.ncbi.nlm.nih.gov/pubmed/?term=.+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



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Multiplier methods


• 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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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.


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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.



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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, 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)

http://www.hivhub.ir/

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Thank You So Much

network scale-up to estimate the population size of high-risk groups for hiv

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