Jessie K Edwards University of North Carolina at Chapel Hill jessedwardsuncedu 1 Perspective To improve public health we must make a series of urgent decisions Resource allocation Intervention strategies ID: 539773
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Extrapolation of data from key population surveys and programs
Jessie K. Edwards
University of North Carolina at Chapel Hilljessedwards@unc.edu
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PerspectiveTo improve public health, we must make a series of urgent decisions.
Resource allocationIntervention strategiesPrioritization Sometimes, we must act without perfect data.
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PerspectiveBut, we rarely act from a position of complete ignorance.
To make decisions, we (often implicitly) summarize any existing information and our remaining uncertainty. This talk walks through some approaches to formalize this process in a specific setting.
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Our SettingDecision makers often need
national estimates of indicators related to key populationsFor advocacy purposesTo set national targets
To inform funding allocation by international bodies4Slide5
What do we want to know?
Size estimates of key populationsFemale sex workersMen who have sex with menTransgender peoplePrevalence of HIV and other diseases
Distribution of risk factors for disease transmissionProgram coverage5Slide6
ChallengesUsually, information to describe a national HIV epidemic has gaps.
Subgroups of the population may be underrepresented
Geographic areas of the country may be excluded6Slide7
We need a principled approach to come up with national estimates when existing data are
incomplete.7Slide8
Ongoing efforts
WHO / UNAIDS meeting in March on strategic indicators for key pops MeSH
co-convened, UNC, LSHTM, JHSPH participants
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Illustrative exampleWhat is the size of the population of female sex workers in the Dominican Republic?
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Overview
Need for a national estimate: Specifically sizes of key populations, including female sex workersFunders request this information
National estimates guide MOH prioritiesInventory of existing data: Size estimates from 37 of 154 municipalities collected in priority provinces using a venue-based sampling approachStatistical approaches to compute national estimate: The focus of this talk.
Consensus building + decision making:
Project is ongoing – due to data security concerns, this example is illustrative only (data are hypothetical)
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Parameter of interest
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In simulations, I set
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Missing data problem
But we only have measurements in a subset of the geographic areas from programmatic data collection in 2014 (let’s denote these regions by
)
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Missing data problem
Our challenge is to use data from the sampled areas to make inference to the entire country
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Approach
Original data:
Because data were collected for programmatic purses,
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In simulated example,
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Approach
Original data:
Expect that
, but did not collect data in all strata of
.
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In simulated example,
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Approach
Original data + targeted additional data collection
Expect that
,
and
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Obtaining information on
The programmatic data collection activities only collected data on .But contextual data (
) can be found other placesCensus informationGeographic databasesOther population health surveys
DHS
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Covariates in
for DR example
From national data (publically available online): Age distributionMale/female ratioEmployment dataPopulation densityPovertyCountry of origin
From DHSAverage number of years of education among womenHIV prevalence
Adolescent pregnancy indicatorsFrom other sources (directly from stakeholders)
Indicator of tourist areaIndicator that the municipality was on a major transit corridor
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Challenges using population-based surveysChallenges with DHS data
Indicators are generalizable to regional level, but we needed municipal estimatesRandom displacement of GPS coordinates for clusters19Slide20
Summarizing DHS data
Create
“surface” by interpolating values of between DHS clusters using a fine grid
Summarize values of by municipality
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Summarizing DHS data
Create
“surface” by interpolating values of
between DHS clusters using a fine grid
Summarize values of
by municipality
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Now a standard missing data problem
We have data on for 50 municipalities
We have data on for all 154 municipalitiesCould use any computation approach for estimating a mean in the presence of missing data. Up-weight existing data to represent distribution of
in entire countryPredict estimates in municipalities without data from a regression model containing
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Here, we use the augmented inverse probability weighted (AIPW) estimator for
.
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The AIPW estimator
The AIPW is a consistent,
semiparametric efficient, and doubly robust, estimator of
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AIPW estimator is consistent, semiparametric efficient, and doubly robust
What does that mean and why should we care?Takes covariates
into accountSmaller variance than traditional weighting approachesTwo chances to correctly specify parametric modelsModel for inclusion in data collection (like weighting approaches) AND model for the outcome of interest (like regression-prediction approaches)In weighting only or regression model only approaches, if the parametric model is
misspecified, results are biased.Here, if
either of these models is correct, estimate of
will be correct
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Data use/consensus building
In this example, stakeholders convened a workshop after the activity to understand the results and Suggest sensitivity analysesWhat if a different set of variables were included in
What if variables in were measured differently?What if variables in were modeled differently?
Discuss areas of uncertainty
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Loose ends
Quantifying uncertaintyHow sure are we that assumptions hold?How much faith do we have in existing estimates of
How well is captured?Are we sure that we have measured all predictors of that differ between municipalities with and without data?Crucial for making decisions (i.e., maximizing expected utility)Best incorporated in a Bayesian framework
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Loose ends
What if we had not been able to collect additional data?That is, what if we had had NO DATA within key strata of
?e.g., What if all data had been collected in urban areas?We could augment our existing knowledge/information using Parametric extrapolationBayesian methods
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DiscussionDecisions can’t always wait for perfect and complete data.
We can use modern statistical tools to produce useful results from incomplete data.Inference may be updated as more information arises.
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This presentation was produced with
partial support from the
United States Agency for International Development (USAID) under the terms of MEASURE Evaluation cooperative agreement AID-OAA-L-14-00004. MEASURE Evaluation is implemented by the Carolina Population Center, University of North Carolina at Chapel Hill in partnership with ICF International; John Snow, Inc.; Management Sciences for Health; Palladium; and Tulane University. Views expressed are not necessarily those of USAID or the United States government .
www.measureevaluation.orgSlide30
Extrapolation of data from key population surveys and programs
Jessie K. Edwards
University of North Carolina at Chapel Hilljessedwards@unc.edu
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