Transcript Fundamental Building Blocks of Social Structure

The Network Scale-Up Method (NSUM)
Christopher McCarty
October 30, 2012
The NSUM team
• H. Russell Bernard (University of Florida)
• Peter D. Killworth (Southampton Oceanography Centre)†
• Christopher McCarty (University of of Florida)
• Eugene Johnsen (UC-Santa Barbara)
• Gene A. Shelley (Georgia State University/CDC)
Outline
• Origin and Evolution of Network Scale-up
Method (NSUM)
• How to do it
• Compromising assumptions
• Current application in international health
Origin and Evolution of Network
Scale-up Method (NSUM)
Populations of Interest
• Public health and public
policy advocates are
interested in certain
populations
• HIV positive
• IV Drug Users
• Migrants
• We know a lot about each
population, but we don’t
know how many there are
• For many reasons these
populations are virtually
impossible to count
• Homeless
• Men Who Have Sex with men
(MSM)
• Female Sex Workers
The Problem
How do you estimate the size
of a population that you
cannot count?
Mexico City Earthquake - 1985
“Everyone seemed to know
someone who died”
–H. Russell Bernard
Government
estimates were
7,000 dead
In a city of 18 million
people, could the
number of dead be
7,000 if everyone
knew someone who
died?
Estimate network size
Estimating the Size of an Average Personal Network and of an Event Subpopulation
H. Russell Bernard, Peter D. Killworth, Eugene C. Johnsen, Scott Robinson
In: The Small World, ed. M. Kochen, 159-75 (1989)
Funded by NSF grant BNS-8318132 and UF Graduate School
• Mexico City divided into 20 grids, 20 respondents per grid,
or 400 respondents
• 91 of 400 (23%) knew someone who died
• But this did not reveal how many people died
• For this they needed to know the personal network size
of each respondent
Relationship between network size
(c) and the probability of knowing
someone who died
• c ≈ (t/e) * p where:
– c = personal network size
– t = total population
– e = subpopulation size (those who died)
– p = probability of being in the
subpopulation
• If e = 7,000, t=18 million and p=.2275 then
c = 585
Back estimate an unknown
Estimating the Size of an Average Personal Network and of an Event Subpopulation: Some Empirical Results
H. Russell Bernard, Peter D. Killworth, Eugene C. Johnsen, Scott Robinson
Social Science Research 20: 109-121 (1991)
Presented at American Statistical Association (1987)
Funded by NSF grant BNS-8318132 and UF Graduate School
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Survey in Mexico City of
2,260 respondents
Solved for the personal
network size that best fit all
subpopulations (c=220)
Applied this to reported
unknown m (rape victims).
Best estimate for the number
of rape victims in Mexico City
(the unknown) was 6,303 ≤ e ≤
8,811
Subpopulation
Event
population
size (e)
Probability of
knowing
someone (p)
Network
size (c)
Doctors
30,426
.3889
173
Mailmen
14,728
.1473
116
Bus Drivers
11,696
.2571
272
Quake Victims
10,000
.2668
332
TV Repairmen
4,013
.2619
810
Priests
1,595
.2854
2254
A Primitive model
Everyone’s network in a society reflects the
distribution of subpopulations in that society
– t = the size of a population
(e.g. the U.S.)
– e = the size of some
subpopulation within it (e.g.
diabetics).
– m = the number of
members of the
subpopulation known by
any one person (e.g.
diabetics)
– c = personal network size
How to do it
This suggests that...
Personal network size
Size of subpopulation
• c=(m*t)/e
• e=(m/c)*t
• where:
– m=sum of all
reported knowns
– t=total population
size
– e=sum of all known
population sizes
• where:
– m=average of all
reported unknowns
– t=total population
size
– c=average network
size
To recap: NSUM is a 2-step process
• Step 1: Estimate personal network size
– Use estimates of the number known (m) in a set of
known populations to estimate network size for each
respondent
• Step 2: Use this to scale-up unknowns
– Use the reports of the unknowns to scale-up to the
unknown population
• Note that under-reporting of known m will result in lower
estimates of c, and bigger e. Under-reporting of unknown
m will result in lower e.
NSUM is a survey approach
• With any survey you must:
– Define the population
– Choose a sample size
– Determine who is an eligible respondent
• With this approach you also must
– Choose a set of populations of known size
Select respondent population
• Respondent population is not the same as the
population to be estimated (target population)
• Respondents are sampled from the population
within which the subpopulation exists
– Interview U.S. respondents to estimate
homeless population in the U.S.
– Interview Barcelona respondents to estimate
heroin users in Barcelona
• You must know the size of the respondent
population (t) (e.g. the U.S. or Barcelona)
Choose sample size
• As in all surveys, sample size should be based on expected
margins of error. If the thing you are estimating varies a
lot, then the sample should be larger to capture that
• Margins of error could be calculated on either network size
(c) or on the values for the unknowns (m)
– Matthew Salganik is working no this problem
• Our data suggest that a survey of 400 respondents would
generate a margin of error of ±26 network alters
• A survey of 1,000 in the U.S. would generate a margin of
error of ±16 network alters
Alter boundary
• Definition of who is an alter can have enormous
effects on the estimate of network size, and
therefore then size of the unknown subpopulation
• Early studies of network size used ever known,
while our studies used currently known
• Our definition:
– You know them and they know you by sight or by
name. You have had some form of contact with them
in the past two years and you could contact them if you
had to
Selecting known populations
• Known populations should vary in size and type
– DO NOT limit study to populations related to, say, health
conditions, although plentiful, may introduce error
– DO NOT use only large populations (such as men or people over
age 65). This introduces recall error
– DO NOT use only small populations. This introduces error from
very few hits
– A rough guide is to use populations between .1% and 4% of the
total population
• Ideally collection of sub-population data will be recurring so that they
can be used in subsequent years
• It is important that the data all reflect the same year (be aware that
some population data lags)
Potential Sources in the U.S.
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U.S. Statistical Abstract
U.S. Census
FBI Crime Statistics
State and city-level Vital Statistics
Surveys by organizations
Voter Registration databases
We experimented with names
• The U.S. Census provides estimates of both first
name and last names
• The advantage of names is that they vary in size
and are typically ascribed
• Countries and cultures vary in the way they use
names
• They are also prone to what’s called barrier error
(more about this in a minute)
Peter D. Killworth, Christopher McCarty, Eugene Johnsen, Gene A. Shelley and
H. Russell Bernard. (1998) A Social Network Approach to Estimating
Seroprevalence in the United States. Evaluation Review 22:289-308
Funded by NSF grant SES-8803577
Estimating the Size of Hard-to-Count Populations
• Telephone survey of 1,524 Florida respondents
• Respondents estimated:
– How many they knew in six known subpopulations (four different
sets of six)
– If they knew someone with certain first names
– How many they knew who tested positive for HIV and those with
AIDS
• Average network size was estimated to be 105 (s.d. 92)
• This approach was limited by using only six known populations for
each respondent and not asking how many people were known with
first names
Peter D. Killworth, Christopher McCarty, H. Russell Bernard, Gene A.
Shelley and Eugene Johnsen. (1998) Estimation of Seroprevalence,
Rape and Homelessness in the U.S. Using a Social Network Approach.
Evaluation Review 22:289-308
• For the next survey we made two critical changes
– We asked each respondent about a lot of known
subpopulations (29 of them)
– We asked how many they knew, not just if they knew
someone in the known subpopulations
• Telephone survey of 1,554 U.S. respondents
• Back-estimation resulted in average network size of 286
s.d. 291 (the first instance of an estimate near 290)
Summation method
• Previous attempts estimated network size by backestimating from known subpopulations
• We thought of an alternative approach:
– Ask respondents how many people they know
in mutually exclusive and exhaustive relation
categories
– Sum the estimates in those categories to get
network size
Relation categories
H. Russell Bernard, Peter D. Killworth, Christopher McCarty. (1982)
INDEX: An Experiment in Social Structure. Social Forces 61:99-133
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Immediate family
Other birth family
Family of spouse or significant other
Co-workers
People at work but don't work with directly
Best friends/confidantes
People know through hobbies/recreation
People from religious organization
People from other organization
School relations
Neighbors
Just friends
People known through others
Childhood relations
People who provide a service
Other
Advantages of the summation method
• Unlike back-estimation from known populations,
the summation method should not be subject to
certain kinds of error (also, more in a minute).
• It does not require finding known populations,
which could be a problem in some countries where
there are no reliable data on the population
Christopher McCarty, Peter D. Killworth, H. Russell Bernard,
Eugene Johnsen and Gene A. Shelley. (2001) Comparing
Two Methods for Estimating Network Size. Human
Organization 60:28-39
• Two telephone surveys in U.S.
• Included back-estimate and
summation method for each
respondent
– 29 known populations for
back-estimating
– 16 relation categories for
summation
• Virtually the same result as
previous study
• Correlation between methods is
either fully or only .56
N
Back
Estimation
Summation
796
290.8
290.7
574
291.2
281.2
1,554 (previous
286
n.a.
study)
Reliability check
Change the data
• We changed reported values at or
above 5 to a value of 5 precisely.
The mean dropped to 206, a
change of 29%.
• We set values of at least 5 to a
uniformly distributed random
value between 5 and 15. We
repeated the random change (5 –
15), but only for large
subpopulations (with >1 million).
The mean increased to 402, a
change of 38% -- in the opposite
direction.
Survey of clergy
• We surveyed a national
sample of 159 members of
the clergy – people widely
thought to have large
networks.
• Mean c = 598 for the backestimation method
• Mean c = 948 for the
summation method
So, 290 was not a coincidence
1. Two different methods of counting similar
results
2. Changing the data produced large changes
in the results, and in the expected
directions
3. People who are widely thought to have
large networks did have large networks
The distribution of c
The data track
Over- and under-estimation
• Tendency for people to
overestimate small populations
(<2 million) and to
underestimate large ones (>3
million).
• The two largest populations are
people who have a twin brother
or sister and diabetics.
• Without these two outliers,
the correlation rises from r =
.79 to r = .94
Compromising assumptions
NSUM Assumptions
1. Everyone in t has an equal chance of knowing someone
in e
– Violation of this is called Barrier Error
2. Everyone knows everything about everyone they know
– Violation of this assumption is called Transmission
Error
3. Respondents can accurately report the number of people
they know in any given subpopulation
– Violation of this is called Recall Error
Barrier Error exists: Correlation between the mean number
of Native Americans known and the percent of the state
population that is Native American is 0.58, p = 0.0001.
Known populations and their relationship to demographic
variables (Barrier Error – there are many dots!)
Population
Native Americans
Gave birth in past 12
months
Adopted a child in past
year
Widow(er) under 65
years
On kidney dialysis
Postal worker
Commercial pilot
Member of Jaycees
Diabetic
Opened a business in
year
Have a twin brother or
sister
Licensed gun dealer
Came down with AIDS
Males in prison
Homicide victim in
past year
Suicide in past year
Died in wreck in past
year
Women raped in past
year
Homeless
HIV positive
State Sex Race Age Education
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Names and their relationship to demographic variables
(Barrier Error – again, many dots)
Population
Michael
Christina
Christopher
Jacqueline
James
Jennifer
Anthony
Kimberly
Robert
Stephanie
David
Nicole
State Sex Race Age Education
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We assumed that a representative sample will eliminate
barrier error. In fact, we don’t know … so far, research to improve
estimates in NSUM is on transmission error.
What to do about Barrier Error?
• We have always assumed we could eliminate the effect of
barrier error by:
1. ...using a large and representative sample of
respondents and
2. ...using a lot of subpopulations
• We don’t know that
• A potential area of research would be to adjust data for
barrier error using information about respondents
Transmission Error
• Recall that errors of transmission occur when you
know someone in a subpopulation but do not
know they are in it
• I might know a diabetic person, but do not know
they are diabetic
• More critical to the method, I might know
someone who is an IV drug user, but not know
they are an IV drug user
Transmission Error Study
• We recruited 30 people who were members of one of the
known populations (e.g. diabetics)
• We elicited 25 alters from each using first names
• The respondent provided the alter’s phone number
(30x25=750 alters)
• We contacted 220 of 750 named alters and asked them
things about themselves and about ego.
Results of Transmission Study
Population
% who
knew
% who did not Respond # of
know
ents
alters
Am. Ind.
100
0
2
12
Diabetic
55
45
6
44
Birth in last 12 mos.
93
7
3
27
Gun dealer
92
8
1
12
Member of JC’s
58
42
1
12
Dialysis
88
12
5
26
Business in last 12 mos.
75
25
4
16
Postal worker
100
0
1
10
Has twin
88
12
2
24
Widowed <65
97
3
4
38
Findings from the alter study
• Some things are more visible (kidney
dialysis versus diabetic)
• Some things are very easy to know (99%
know marital status of ego)
• Some things are hard to know (52% knew
how many siblings ego had)
Some people withdraw
Gene A. Shelley, Peter D. Killworth, H. Russell Bernard, Christopher McCarty,
Eugene C. Johnsen, Ronald E. Rice. (2006) Who knows your HIV status II:
Information propagation within social networks of seropositive people.
Human Organization 65: 430-445
• Gene Shelley conducted ethnographic work with a
sample of HIV+ respondents
• People said that they withdrew from their network
in order to limit the number of people who knew
their HIV status
• Eugene Johnsen confirmed that HIV+ people
have, on average, networks that are one-third the
average of others in the U.S.
What to do about Transmission Error?
• From the transmission study we tried to develop weights
for classes of characteristics about subpopulations …
– Things that carry a strong stigma (HIV+)
– Things that carry a moderate stigma (diabetes)
– Things that just don’t come up in conversation (being a twin)
• These weights did not improve our estimates
• Recent work in this area focuses on transmission error on
the unknown m values (e.g. HIV, drug user)
Our estimates using NSUM
• Killworth et al. 1998 of 1,554 adults in the U.S. in
1994.
– HIV+: 800,000 ± 43,000
– Homeless: 526,000 ± 35,000
– Women raped in the last 12 months: 194,000 ± 21,000
• These were all close to other estimates
Charles Kadushin, Pater D. Killworth, H. Russell Bernard and Andrew a.
Beveridge (2006) Scale-up Methods as Applied to Estimates of Heroin
Use. Journal of Drug Issues. 36: 417-439
• National survey n=17000
to estimate the prevalence
of crimes in 14 cities,
large and small, in the
U.S.
• Reported the number of
people they knew who had
been victims of six kinds
of crime and the number
of people they knew who
used heroin regularly.
Tian Zheng, Matthew Salganik and Andrew Gelman (2006) How many people do
you know in prison?: Using overdispersion in count data to estimate social
structure in networks. Journal of the American Statistical Association 101(474)
474: 409-423
• Re-analyzed NSUM data from Killworth et al. (1998)
• Developed a method to estimate social structure using
NSUM survey data
– Respondents varied in the number of acquaintances
– Respondents varied in propensity to form ties to people
in some groups, and not in others
• This was a critical turning point as this was the first
independent group to work on statistical improvements to
the method
Current application in
international health
MARPs
• Certain populations are at high risk for contracting and
spreading HIV
• Most At Risk Populations (MARPs) typically fall into one of
three categories
– Female Sex Workers
– Men Who Have Sex With Men
– IV Drug Users
• Much is known about the prevalence of HIV among these
groups, but not much about the size of the groups, especially
at the country level
Methods to Estimate the Size of MARPs
(http://data.unaids.org/pub/Manual/2003/20030701_gs_estpopulationsize_en.pdf)
• Methods that require a sample frame
– Census
• Counting all members
– Enumeration:
• Counting members in a sample frame then scaling up
– Population Survey:
• Draw a representative sample (similar to enumeration)
• Methods that do not require a sample frame
– Capture-Recapture
– Multiplier
Problems with these approaches
• All these methods require interviews with members of the
target population
• The Census, Enumeration and Population Surveys require
sample frames which are lacking for hidden or elusive
populations
• The Capture-Recapture and Multiplier methods are
difficult to do across large geographies
• NSUM was viewed as a way to triangulate these estimates
WHO and UNAIDS and
international HIV surveillance
• Rob Lyerla (UNAIDS) and Kevin O’Reilly (WHO)
worked in global HIV/AIDS surveillance
• They were looking for a way to estimate the size of at-risk
populations at the country level
• They convened a workshop in 2008 to discuss the
possibility of using NSUM
Advantages of NSUM
• Does not require contact with target populations
who may be difficult to reach
• Can be done at city, region or country level
• Should be relatively inexpensive compared to
other methods
• Can use back-estimation of known populations to
validate estimates
NSUM has been applied in several
countries since 2008
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Thailand
Brazil
Rwanda
Ukraine
Moldova
China
Japan
• There have been
innovations in some of
these in attempts to
improve the method
Ukraine
•
Face to face survey n=11,000 (500 per oblast)
•
NSUM estimates sensitive to under-reporting on unknown m values
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Added scale of perceived stigma to adjust for transmission error on unknown
m values
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Using perceptions of population membership stigma to weight the m values
Numbers are political!
• NSUM estimates were reasonable for some populations,
not for others
• Estimates of IV drug users were lower than other estimates
• Groups with investment in numbers — NGOs, goverment
agencies, journalists
• Controversy with MSM (men who have sex with men)
population as the method does not involve interviews with
target– (Nothing about us without us!)
Thailand
• National survey of 8,600
• Used procedure to create mutually exclusive and
exhaustive categories of knowing in native language for
summation method
• Estimates of IV drug users were within expectations
• Estimates of migrants were far below prevailing estimates
• Prevailing estimates may be a challenge
Brazil
Salganik, M.J., Mello, M.B., Abdo, A.H., Bertoni, N., Fazito, D., and
Bastos, F.I. (2011) The Game of Contacts: Estimating the Social
Visibility of Groups. Social Networks Vol.33 No.1, pp.70-78.
• Survey of 500 in Curitiba, Brazil
• Used game of contacts
– Respondents in the target population (drug users) were presented
with first names.
– If they knew a name they then identified if the person was or was
not a drug user
– Also reported if respondent knew they were a drug user
• This resulted in a measure of transmission error that could be used to
up-weight estimates
Rwanda
• Survey n=5,000 by region
• Attempt to use recent Demographic Health Survey (DHS)
to create known populations in places where they are
difficult to get
• Used alternative definitions of knowing
• Know by sight or by name
• Would eat a meal with
Cost Estimates
Country
Cost USD
Sample size
Cost per complete USD Mode
Geography
USA
10,500
1500
7
Telephone Country
Japan
10,000
1500
7
Web
Country
China
25,000
3000
8
FTF
City
Kazakhstan 10,000
1200
8
FTF
City
Thailand
130,000
8600
15
FTF
Country
Ukraine
180,000
11000
16
FTF
Country
Brazil
20,000
500
40
FTF
City
Rwanda
400,000
5000
80
FTF
Country
Current efforts
• Most current research is focused on transmission error of
estimates for the target population
• So far it has been applied in circumstances where known
populations sizes are available
• More research is needed in refining the protocol for
collecting the data
• Statisticians are working on modeling efforts to improve
estimates with existing method
Thank You
Questions?