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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.

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Presentation on theme: "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."— Presentation transcript:

1 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 Institute UCSF, San Francisco, CA, USA Regional Knowledge Hub, and WHO Collaborating Center for HIV Surveillance, Kerman University of Medical Sciences, Kerman, Iran Methods Core Seminars – Center for AIDS Prevention Studies/UCSF – 20 Sep. 2013

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 2

3 Part 1 PSE methods overview 3

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 4

5 How to do MARP size estimates? Cost Scientific rigor 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 5

6 Cost Scientific rigor Straw man Conventional Wisdom Borrow from thy neighbor Soft modeling Consensus Wisdom of the crowds Delphi Registries, police, SHC, drug treatment, unions, workplace Discrepancies Place, RAP, ethnography Unique event multiplier Truncated Poisson Multipliers, multiple multipliers Multiple sample recapture Capture-recapture Network scale up Population-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 6

7 Direct Methods Done with surveys of MARPs 7

8 Direct questions to population-based surveys StrengthsWeaknesses 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):

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 Jul 23. [Epub ahead of print]AIDS Behav. 9

10 Census and enumeration StrengthsWeaknesses 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 10

11 Capture-recapture 11

12 Capture-recapture StrengthsWeaknesses 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 correlated 2)each population member should have equal chance of selection 3)each member must be correctly identified as ‘capture’ or ‘recapture’ 4)no major in/out migration 12

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 Dec 1;41(12):60-5. Print 2012.Iran J Public Health. 13

14 Nomination method StrengthsWeaknesses 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 14

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 Apr;40(4): Sex Transm Dis. 15

16 Multiplier methods StrengthsWeaknesses 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 16

17 Indirect Methods Done in surveys of the general population 17

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

19 Proxy respondent method StrengthsWeaknesses 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 19

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):

21 Network scale-up StrengthsWeaknesses 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 21

22 Part 2 NSU method overview 22

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. 23

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)? 24

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 25

26 NSU – Frequency Approach 26

27 NSU – Probability Approach 27 Probability ApproachFrequency 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 28

29 Confidence Interval - Bootstrap 29

30 Size Estimation of Groups at High Risk of HIV/AIDS using Network Scale Up in Kerman, Iran 30 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): Kerman T = 132,651 Age Male

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)? 31

32 Example – Data Collection Tool 32

33 Part 3 Network size estimation 33

34 Definition of social network Global network Active network Supportive network Sexual network Sub-networks – Family – Coworkers – Classmates – Sport – … 34

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 AND Living in your area/country AND ……….. 35

36 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 – …….. 36

37 Disadvantages of direct methods Reliability and validity issue Double counting in summation method 37

38 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 38

39 C – Network Size 39

40 Specific criteria for reference groups Prevalence between 0.1-4% one-syllable name Stable prevalence over time and in different ethnicities 40

41 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 41

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

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

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

45 Ratio Method – Kerman NSU 45 # StepsCMin-RatioMax-Ratio Removing group Step Step m8 Step m1 Step m7 Step m5 Step m12 Step m21 Step m10 Step m19 Step m11 Step m16 Step m9 Step m15 Step m20 Step m14

46 Ratio Method – Kerman NSU 46 Plot real versus predicted size of reference groups variableRealEstimateRatio m m m m m m m m m

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) 47

48 Regression Method – Iran NSU 48

49 Regression Method – Iran NSU STATA commands: reg meanm propm, beta dfbeta disp 3/sqrt(23) 49 idpropmmeanm_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 50 Final Network Size Ratio M. 380 Regression M. 308 Glob J Health Sci.Glob J Health Sci 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 Part 4 Correction for biases in NSU 51

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. 52

53 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 53

54 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 54

55 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 55 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

56 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. 56

57 Game of contacts: transmission rate 57

58 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 58

59 Game of contacts: popularity ratio 59

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%) 60

61 Visibility and Popularity Factors – NSU Iran 61 Iran NSU Pop. Size Estimates (Point Estimate) Pop. Size Estimates (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 ( ) Sample size = people (400 per province) Total Pop size = 75,149,699

62 Validation Study: Social Desirability Bias 62 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): 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: 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 Jun 17;5(4): 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): 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:ii (publications) 63

64 Thank You So Much 64


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