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ASSESSING THE QUALITY OF POPULATION SIZE ESTIMATES OF PEOPLE wHO INJECT dRUGS (PWID). Waimar Tun, Population Council 20 th International AIDS Conference Melbourne, Australia July 20 – 25, 2014. Background. - PowerPoint PPT Presentation
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ASSESSING THE QUALITY OFPOPULATION SIZE ESTIMATES OF PEOPLE WHO INJECT DRUGS (PWID)Waimar Tun, Population Council
20th International AIDS Conference Melbourne, AustraliaJuly 20 – 25, 2014
Background• Current PWID population size
estimations (PSE) in many countries are not based on strong data
• UNODC and World Bank requested a review of existing PWID population size estimates in 10 countries – Belarus, China, India, Libya, Myanmar,
Philippines, Kazakhstan, Kyrgyzstan, Tajikistan, & Uzbekistan
Learning objectives• Be able to critically review existing
estimates and their methodology
• Be able to identify opportunities to improve the estimates (if required)
• Understand the strengths and weaknesses of methods of PSE data collection
Where do I get my data?• Published and grey literature (including
HIV- and drug- related country reports)
• Discussions with stakeholders in-country (NAP/NAC, UNAIDS, WHO, UNODC, CDC, USAID, PWID representatives, civil society organizations)
• Stakeholder meeting with representatives from MoH, drug control agencies, civil society, and implementing partners
Comment on data sources• PSE methodologies not or poorly
specified• Reports not translated or translations
of technical terms were ambiguous• Stakeholders not always aware of
PSE activities happening in their own country
• Time-consuming
What should I consider when I review the quality of the estimates?
• Are the underlying assumptions of the method met?
• What are the potential biases and how do they impact the estimate?
• Are multiple methods used?• What is the quality of the data used?
Common methods to estimate PWID population size
Literature review/desk exercise
• Strengths:– Low cost– Little
time/resources required
• Weaknesses:– Local contexts may
be very different– Data sources may
not be based on rigorous methods
• Review published and grey literature for similar context and geographic region
• The benchmark from literature is applied to the adult male and female population
Delphi
• Strengths:– Utilizes local expert
views and experience– Does not require raw
data capture– May be only option
for countries with limited data sources
• Weaknesses:– Sometimes based
only on qualitative or anecdotal information
– Not good for identifying trends, comparing to other regions
• Systematically solicits and reviews selected experts’ estimates
• Iterative process through a series of feedback and revisions
Mapping/census and enumeration
• Strengths:– Is a real count, not
an estimate– Can produce a
credible lower limit
• Weaknesses:– PWID not always accessible,
may not be exposed to census data collectors
– Assumes you can locate all PWID
– PWID may not wish to reveal drug use due to stigma and/or legal concerns
– Highly time consuming and expensive
– Safety issues; dangerous hotspots
• Venues where PWID congregate are identified • Census counts all PWID at all hotspots• Enumeration counts PWID at a sample of sites
Capture-recapture
• Strengths:– Fairly easy to
implement in short period of time
– Usually cost-efficient to implement
– Can be done with multiple service sources
• Weaknesses:– Assumptions of
method difficult to meet in reality (independent samples)
– May be dangerous to implement (unsafe hotspots)
• PWID are counted and ‘tagged’; a 2nd count is conducted
• An estimate is obtained through a formula that includes captures and overlaps between the 2 rounds
Service multipliers
• Strengths:– Uses existing and
available data from service providers
– Easily incorporated into IBBSS with minimal additional questions
• Weaknesses:– High-quality service
data may not be available • No duplicates• Each target population
member must have chance of being included in service data
• Requires two data sources:1. Benchmark (service data such as drug treatment or HIV testing)2. Population-based survey with PWID where you obtain info on the
proportion who report that point of contact (‘benchmark)Estimate obtained from multiplying inverse of proportion to the benchmark
Wisdom of the crowd
• Strengths:– Very easy to
implement. Only one question.
– Easily incorporated into IBBSS or other size estimation methods
• Weaknesses:– May be biased if large
segment of population is not well-networked or “hidden”
– Bias if not implemented in a representative survey
• Ask PWID survey participants to estimate the number of PWID in a given location
• Assumes that the average response approximates the actual number
RDS† successive sampling size estimator‡
• Strengths:– Easily calculated with
existing RDS survey data
• Weaknesses:– Statistical validity
currently under debate– Not recommended as a an
“only” method of estimation.
– Results may be biased depending on number of people surveyed and actual population size
• In RDS-based survey, respondents indicate their network size
• Modelling is based on the assumption that those with large networks are sampled first and that the population will be depleted at a certain point
† Respondent-driven sampling; ‡ Handcock, Gile, Mar (2012)
Network scale-up method† (NSUM)
• Strengths:– Does not ask
sensitive questions directly to respondent
– National level estimate
• Weaknesses:– Average personal network
size difficult to estimate – Some PWID may not interact
much with members of the general population
– Respondent may not be aware that someone in their network engages in injection drug use
• Uses general population survey; questions about:• Number of people they know of a known population• Number of PWID they know
† Bernard, Killworth, Johnsen, and Robinson (1991)
Triangulation of multiple methods• Data points from multiple methods
are desirable when possible– Reduces bias from any single method–May provide plausible lower and upper
bounds– Informs stakeholder debate– Facilitates consensus on estimate
ranges
PWID population size estimation (2011 IBBSS, Nairobi)
STD HIV testing 1 Literature review
Drop-in HIV testing 2 WOTC0
5,000
10,000
15,000
20,000
25,000
20,833
13,250
6,562 5,869 5,0313,000
Median 6,107 Lower plausible 5,031
Upper plausible 10,937 (~0.5% adults)
Source: Population Council, UCSF, NASCOP/Kenya, CDC (2011)
RDS SS-Size Added
STD
HIV testin
g 1
RDS SS-S
ize
Litera
ture r
eview
Drop-in
HIV testin
g 2WOTC
0
5,000
10,000
15,000
20,000
25,00020,833
13,25011,463
6,562 5,869 5,0313,000
Source: Population Council, UCSF, NASCOP/Kenya, CDC (2011)
Lower plausible 5,031Median 6,107
Upper plausible 10,937 (~0.5% adults)
Cost
Scie
ntifi
c rig
or
Straw manConventional Wisdom
Borrow from thy neighborSoft modeling
Consensus
Wisdom of the crowdDelphi
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, key informants, observation counting
Scientific rigor and costs of methods
Source: University of California, San Francisco
RDS – Sequential Size
Cost
Scie
ntifi
c rig
or
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, key informants, observation counting
No resources or opportunity for data collection
Source: University of California, San Francisco
RDS – Sequential Size
Cost
Scie
ntifi
c rig
or
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, key informants, observation counting
Data collected directly from PWID for size estimation purposes only
Source: University of California, San Francisco
RDS – Sequential Size
Cost
Scie
ntifi
c rig
or
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, key informants, observation counting
Data collected from general population (DHS, AIDS Indicator Survey)
Source: University of California, San Francisco
RDS – Sequential Size
Cost
Scie
ntifi
c rig
or
Straw manConventional Wisdom
Borrow from thy neighborSoft modeling
Consensus
Wisdom of the crowdDelphi
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, key informants, observation counting
Data from PWID collected for other purposes (IBBSS, registries, service data)
Source: University of California, San Francisco
RDS – Sequential Size
IBBSS integration• Size estimation methods increasingly
being integrated worldwide– Leverages existing resources– Adds value to behavioral and seroprevalence data
already being collected• RDS increasingly used for IBBSS
recruitment– “Population-based” estimates
• Forthcoming RDS software-based estimation (SS-Size)– Provides an estimate using existing IBBSS RDS network
data– Has limitations and caveats, should not be used as a
sole estimate source
Other considerations• Ethical reviews• Administrative approvals• Safety of research assistants/study
team• Involvement local drug using
community representatives
Conclusion• Important to review how researchers
arrived at the estimate since many are not grounded in quality data
• Multiple PSE methods should be used• PWID size estimation should be a
part of routine surveillance• Stakeholder consensus on estimate
ranges critical
Acknowledgement• Scott Geibel (Population Council)• Henry Fisher Raymond (UCSF)• Abu Abdul-Quader (CDC)• Pandu Harimurti (The World Bank)• Riku Lehtovuori (UNODC)
GROUP DISCUSSION
Country A• Epidemic concentrated in PWID (account for
~90% of HIV transmission)• HIV prevalence in PWID: 15-30% (up to 87% in
one city)• Civil unrest has hindered PSE of PWID• PSE (2,000) based on government registration
of drug users† (0.05% of population nationally)• RDS-based IBBSS was conducted in 2013 in
one city; no PSE• No IBBSS planned for near future
† Registries based on treatment registers to arrest counts.
Country B• Epidemic concentrated in PWID, FSWs and their
clients; PWID HIV prevalence: 7%• PSE available for 25 out of the country’s 28
states• National PSE (177,000; 0.02% of population)
obtained through:– District-level mapping/enumeration at hotspots – Data updated regularly (by NGOs that implement
targeted intervention at hotspots (WOTC with PWID and gatekeepers at hotspots)
• IBBSS conducted every 2-3 years; currently being conducted but no PSE
Country C• HIV epidemic concentrated in PWID (account for ~60% of
HIV transmission)• Prevalence of PWID: 100,000-200,000 (~0.9% of
population)• Has extensive epi-behavioral data, including PSE;
regulated by government• Latest published PSE available from 2010/11
– Methods and quality varies across region– National PSE obtained from summing regional results
• Current/Upcoming activities:– 2014 RDS-based IBBSS with service multiplier (6 sites); 7
multipliers being used– Some regions will include cap-recap with independent databases– NSUM (2012/13)
Country D• Epidemic concentrated (PWID, MSM, FSW/clients)
– PWID HIV prevalence: 18%• Prevalence of PWID
– Range: 60,000-195,000 – Stakeholder consensus (2002): 75,000 – Estimate based on 0.5% of the population being PWID;
this may be based on registration of drug users• IBBSS completed in May 2014; includes PSE using
service and unique object multiplier– Conducted in 16 sites (14 are in high opium-growing
states and two are major urban centers)– Injection drug use is believed to be occurring outside of
these sites as well
Questions for Group Work• What are the potential
problems/biases with the current estimate?
• What kind of opportunity can you identify for improving the estimate? What are possible next steps?