Presentation on theme: "Ali Mirzazadeh MD. MPH. PhD."— Presentation transcript:
1 Ali Mirzazadeh MD. MPH. PhD. 9/20/2013Network Scale-Up to Estimate the Population Size of High-Risk Groups for HIVMethods Core Seminars – Center for AIDS Prevention Studies/UCSF – 20 Sep. 2013Ali Mirzazadeh MD. MPH. PhD.Institute for Health Policy Studies / Global Health Sciences Institute UCSF, San Francisco, CA, USARegional Knowledge Hub, and WHO Collaborating Center for HIV Surveillance, Kerman University of Medical Sciences, Kerman, IranNSU Workshop (Overview PSE - AM)
2 This presentation has the following parts P1: PSE methods overviewP2: Network Scale-up method overviewP3: Network size estimationP4: Correction for biases in NSU
4 Why do MARP size estimates? Know, track, and predict your epidemicDisproportionate impact in low level, concentrated, and generalized epidemicsProgram planningAdvocacy, development, M&EBecause you were asked toUNAIDS, UNGASS, PEPFAR, MOHResource allocationRight population, right priority, right amount on right programs
5 How to do MARP size estimates? There is no gold standard, no censusWe do not know which method is bestWe are not able to fully calibrate or correctMany methodsScientific rigorCost
6 Done in surveys of the general population CensusPopulation-based surveyNetwork scale upOil wellsDone with surveys of MARPsMultiple sample recaptureCapture-recapturePlant recaptureUnique object multiplierTruncated PoissonScientific rigorMultipliers, multiple multipliersUnique event multiplierMapping with census and enumerationNomination countingPlace, RAP, ethnographyRegistries, police, SHC, drug treatment, unions, workplaceWisdom of the crowdsDelphiConsensusDone by literature review, experts, stakeholders, modelsDiscrepanciesSoft modelingBorrow from thy neighborConventional WisdomStraw manCost
7 Done with surveys of MARPs Direct MethodsDone with surveys of MARPs
8 Direct questions to population-based surveys StrengthsWeaknessesSurveys are familiarEasy if a survey is underwayStraightforward to analyzeSampling is easy to defend scientifically (“gold standard”)Low precision when the behaviors are rareRespondents are unlikely to admit to stigmatized behaviorsOnly reaches people residing in householdsPrivacy, confidentiality, risk to subjectsMirzazadeh 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 StrengthsWeaknessesIt is a real count, not an estimateCan produce credible lower limitCan be used to inform other methodsUse in program planning, implementation, evaluationAt-risk populations hidden, methods miss some members (China: multiply by 2 – 3!)Stigma may cause members to not identify themselvesTime-consuming and expensiveStaff safetySubject safety
12 Capture-recapture Strengths Weaknesses Relatively easy Does not require much dataWhen no other data or studies are available4 conditions hard to meet:two samples must be independent , not correlatedeach population member should have equal chance of selectioneach member must be correctly identified as ‘capture’ or ‘recapture’no major in/out migration
13 Nomination methodS 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 methodsNeed the target group to be connected/networkTime consumingBroken chainsBiased 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 methodsWhen already doing an IBBSSTwo sources of data must be independentData sources must define population in the same wayTime periods, age, geographic areas must alignInaccuracy of program data and survey data
17 Done in surveys of the general population Indirect MethodsDone in surveys of the general population
18 Proxy respondent method Proxy Respondent (Alter)RespondentMember ofHidden Pop.Mirzazadeh A, Danesh A, Haghdoost AA. Network scale-up and proxy respondent methods in prisons [ongoing]
19 Proxy respondent method StrengthsWeaknessesEstimates from general population rather than hard-to-reach populationsDoesn’t require directly asking sensitive questions or lengthy behavioral surveySome subgroups may not associate with members of the general populationRespondent may be unaware the alter engages in the behavior of interestBiases may arise by types of questions asked
20 Network scale-upShokoohi M, Baneshi MR, Haghdoost AA. Size Estimation of Groups at High Risk of HIV/AIDSusing 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 populationsDoesn’t require directly asking sensitive questions or lengthy behavioral surveyAverage personal network size difficult to estimateSome subgroups may not associate with members of the general populationRespondent may be unaware someone in network engages in the behavior of interestBiases may arise by types of questions asked
23 NSU Basic ConceptsA random sample of the general population describes their social networksnetwork sizes (C)the presence of individuals belonging to special sub-populations of interestBased 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 QuestionsHow 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 tC= one individual’s acquaintances (or personal network size)m = the number of individuals belonged to the target population among those acquaintancesE = the hidden population with the size of e
30 Size Estimation of Groups at High Risk of HIV/AIDS using Network Scale Up in Kerman, Iran Age MaleShokoohi M, Baneshi MR, Haghdoost AA. Size Estimation of Groups at High Risk of HIV/AIDSusing 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)?
33 Network size estimation Part 3Network size estimation
34 Definition of social network 9/20/2013Definition of social networkGlobal networkActive networkSupportive networkSexual networkSub-networksFamilyCoworkersClassmatesSport…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.ANDWith whom you have contacted over the last two years in person, or by telephone orLiving in your area/country………..
36 Direct methods Overall question? How many do you know? 9/20/2013Direct methodsOverall question? How many do you know?Active networkSupporting networkSub-networksSub-groups (summation method)FamilyCoworkersSportsEx-classmatesClubsChurch……..NSU Workshop (Network Size Estimation - AAH)
37 Disadvantages of direct methods 9/20/2013Disadvantages of direct methodsReliability and validity issueDouble counting in summation methodNSU Workshop (Network Size Estimation - AAH)
38 9/20/2013Indirect methodsC is estimated based on the frequency of members belonging to a sub-populations with known sizes (reference groups):Number of birth in last yearNumber of death due to cancer/car accident in last yearNumber of marriage in last yearNumber of people with specific first nameIt is a type of back calculationNSU Workshop (Network Size Estimation - AAH)
40 Specific criteria for reference groups 9/20/2013Specific criteria for reference groupsPrevalence between 0.1-4%one-syllable nameStable prevalence over time and in different ethnicitiesNSU Workshop (Network Size Estimation - AAH)
41 Back calculation of the size of reference groups 9/20/2013Back calculation of the size of reference groupsAt least 20 reference groups are needed in the first stepSome of these reference group may generate bias estimatesStep by step, non-eligible reference groups has to be detected and dropped form the calculation:Ratio MethodRegression MethodNSU Workshop (Network Size Estimation - AAH)
42 Ratio method algorithm Step 1: including all reference groups, calculate CStep 2: back-calculate the size of all reference groups (given C)Step 3: calculate bias ratio [(Real size/Estimated size)–1] for every reference groupStep 4: exclude the most biased reference group, and recalculate CStep 5: back-calculate the size of all remaining reference groups (given new C)Step 6: recalculate bias ratio for every reference groupStep 7: check if all bias ratios are between 0.5 and 1.5Step 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 1Calculate the network sizeC_estimation(withoutsolution).xml
44 Real vs. predicted size for 23 Ref. groups – Kerman NSU
46 Ratio Method – Kerman NSU Plot real versus predicted size of reference groupsvariableRealEstimateRatiom24784231.17m36100181.14m42527860.73m61372001.33m132069420.75m171197841.05m182495921.41m22813210.57m23738000.71
47 Regression MethodNSU 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)
50 Real vs. Predicted Size Ratio and Regression Methods Final Network SizeRatio M. 380Regression M. 308Glob 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 4Correction for biases in NSU
52 Main Biases in NSUTransmission 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) 9/20/2013NSU 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 sizeNSU Workshop (Plausibility And Correction Factors - AAH)
54 NSU adjustment factors (2) 9/20/2013NSU 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 sizeNSU Workshop (Plausibility And Correction Factors - AAH)
55 NSU adjustment factors (3) 9/20/2013NSU 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 sizeMirzazadeh 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):623-31NSU Workshop (Plausibility And Correction Factors - AAH)
56 Game of contacts: transmission rate 9/20/2013Game of contacts: transmission rateIn 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 9/20/2013Game of contacts: transmission rateNSU Workshop (Plausibility And Correction Factors - AAH)
58 Game of contacts: popularity ratio 9/20/2013Game of contacts: popularity ratioThe game of contacts estimates the relative personal network size of members of the high-risk population and the general populationSelecting a list of first namesAsking from a sample of the general population how many people they know with one of the selection first namesAsking from a sample of the target population how many people they know with one of the selection first namesNSU Workshop (Plausibility And Correction Factors - AAH)
59 Game of contacts: popularity ratio 9/20/2013Game of contacts: popularity ratioNSU Workshop (Plausibility And Correction Factors - AAH)
61 Visibility and Popularity Factors – NSU Iran Sample size = people (400 per province)Total Pop size = 75,149,699Iran NSUPop. Size Estimates(Point Estimate)(95% CI)Prevalence (95% CI)Alcohol1,300,858(1,195, ,426,513)1.73 ( )Opium1,101,411(973, ,273,240)1.47 ( )Opium sap (Shireh)493,156(437, ,938)0.66 ( )Amphetamine, ecstasy and LCD224,357(205, ,362)0.30 ( )Cristal439,861(387, ,428)0.59 ( )Heroin / Crack262,344(235, ,184)0.35 ( )Marijuana / Hashish352,592(311, ,857)0.47 ( )Any drug injection207,722(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):623-31Mirzazadeh 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 ResourcesRastegari 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):217-27Shokoohi 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)