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Capture-recapture methodology for estimating hard-to-reach - - PowerPoint PPT Presentation

I NTRO C ENSUS C APTURE -R ECAPTURE A SSUMPTIONS U SUAL R ESIDENTS O THER M ETHODS AND PRACTICES Capture-recapture methodology for estimating hard-to-reach populations: Estimating the number of usual residents in the Netherlands 2010 S.C.


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INTRO CENSUS CAPTURE-RECAPTURE ASSUMPTIONS USUAL RESIDENTS OTHER METHODS AND PRACTICES

Capture-recapture methodology for estimating hard-to-reach populations: Estimating the number of usual residents in the Netherlands 2010

S.C. Gerritse

Statistics Netherlands

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SUSANNA GERRITSE

Bachelor Clinical Psychology (University of Amsterdam) Research Master Psychology, major in Methodology (University of Amsterdam) PhD candidate (Utrecht University, Collaboration with Statistics Netherlands).

"Capture-recapture methodology on register data for quality report for the Census "

Extracuricular: PhD Network Netherlands

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PHD PROJECT

  • Prof. Dr. Bart F. M. Bakker

Register and Administrative data

  • Prof. Dr. Peter G. M. van der

Heijden Statistics . My PhD: Capture-recapture methodology

Sensitivity analyses on assumptions methodology

Application to quality report of Census 2011

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GOAL

What are the number of usual residents in the Netherlands? Residing in the Netherlands for longer than 12 months Why important to this Summer school? Hard-to-reach sub populations Solution: Census Quality Census?

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CENSUS

Eurostat: every ten years each European country has to conduct a Census Conducted by National Statistical Institute (NSI’s). Complete overview population Provides information to develop policies, plan and run public services, and allocate funding. Why do we need this?: How many homeless people are there? → Do we need more shelters? How many people or different ethnicities do we have? → Evaluating equal opportunities policies How many people live in the capital city? → Do we need to build more (student-)housing?

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CENSUS

How can we conduct a Census? Traditional Census Enumerate every individual Either internet form or door-to-door poll officers

Advantages: Enumerate almost everyone Disadvantages: Costly, a burden on the resident, non-response Countries: UK, Ireland, Portugal, Russia, Greece, etc.

Rolling Census: Traditional Census spread out Enumerating different characteristics over multiple years

Advantages: higher frequency of Census data Disadvantages: Costs, more burden France, US.

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CENSUS

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CENSUS

Register based, administrative census. One or more registers Most important: Population register

Advantages: Less costs, data already exists, no burden on the residents, higher frequency Disadvantages: Data are collected for other reasons. Finland, Norway, Sweden

Integrated Census: Administrative data and enumeration Combination of Administrative data and additional survey’s

All the advantages of admin data, plus extra information via survey Netherlands, Belgium, Germany, Poland, Spain, Italy, Israel.

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CENSUS

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HARD-TO-REACH-POPULATIONS

Dutch Census: Since 1995: Automated population registration system Most important: Population register based on data from municipalities (municipalities own the data) The Population Register is incomplete → Undercoverage

Free movement and residence within EU Illegal immigrants

Undercoverage PR What is the quality of the PR? How can we estimate the hard-to-reach population? → Capture-recapture estimation Note: Overcoverage.

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OVERVIEW

Overview: Capture-recapture methodology Outcomes my PhD Other methods to estimate hard-to-reach populations, examples in Europe

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CAPTURE-RECAPTURE

Population size estimation technique Originated from animal estimation The Tundra Vole: In the ’Turfzakken’ polder, in 2009: On 10 different locations, in total 20 cages. Captured voles were noted and information collected. Some hair on their back was removed - Tag. A week later they repeated the process Three counts: Voles caught in the first sample Voles caught in the second sample Voles caught in both camples

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CAPTURE-RECAPTURE

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CAPTURE-RECAPTURE

Human estimation? Already existing administrative data Linking two (incomplete) registers PR, Dutch Population Register containing the registered population CSR, Crime Suspects register, Police register on suspects of known

  • ffenses

Table: Expected values

CSR PR 1 1 m11 m10 m01 m00

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INDEPENDENCE

Under independence Loglineair model log mij = λ + λA

i + λB j

where λA

1 = λB 1 = 0.

Odds ratio m00m11 m10m01 = 1 Two ways to estimate m00: Poisson loglinear regression: ˆ m00 = exp(ˆ λ) Maximum likelihood estimate (mle): ˆ m00 = ˆ m10 ˆ m01 ˆ m11 = n10n01 n11 .

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ESTIMATING THE VOLES

Table: Population voles

Sample 1 Sample 2 1 1 2 6 6 ?

Odds ratio m11m00 m10m01 = 1. ˆ m10 ˆ m01 ˆ m11 = ˆ

  • m00. → n10n01

n11 = ˆ m00. 6 ∗ 6 2 = 18. → 6 + 6 + 2 + 18 = 32

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CAPTURE-RECAPTURE

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ASSUMPTIONS

Assumptions Independence between registers Perfect linkage of individuals in registers No erroneous captures Heterogeneous inclusion probabilities Closed population Two issues: How do you meet the assumptions? Effect of violation on population size estimate?

Literature? Sensitivity analyses

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DATA

Compare two different nationality groups. Afghan, Iraqi, Iranian

Need visum for the Netherlands

Polish

EU → free movement and residence Table: Observed values and estimate ˆ m00

CSR PR 1 1 1,085 26,254 255 6,170.3

(a) Population of people with an

Afghan, Iraqi and Iranian (AII) nationality residing in the Netherlands in 2007

CSR PR 1 1 374 39,488 1,445 152,567.3

(b) Population of people with a Polish

nationality residing in the Netherlands in 2009

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INDEPENDENCE

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PERFECT LINKAGE

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ERRONEOUS CAPTURES

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IMPLIED COVERAGE

Why is there such a difference between the nationality groups? Implied coverage

Table: Observed counts mle ˆ m00

HKS GBA 1 1 1,085 26,254 255 6,170.3

(a) Afghan, Iraqi, Irani nationality in

the Netherlands, 2007

HKS GBA 1 1 374 39,488 1,445 152,567.3

(b) Polish nationality in the Netherlands,

2009

Overlap between the registers Coverage of the PR, given the CSR. Low implied coverage → High estimation → Not robust High implied coverage → Low estimation → Robust.

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METHOD

What is the number of usual residents in the Netherlands? We used three registers. Population register - PR Crime Suspects Register (police) - CSR Employee Register - ER Important: Length of stay PR: Difference between Census date and date of registration

However: present before date of registration? Assumption: Everyone registered in the PR has the intention to stay for 12 months

ER - job lengths

Assumption: Residence in NL during the time of the job Unemployed between jobs?

CSR - ?

Impute.

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UNDERCOVERAGE PR

Three registers (PR, CSR, ER) Multiple covariates (Sex, Age, Usual Residence, and nationality group) Deterministic en probabilistic linkage Delete erroneous captures PR and ER: Census date CSR: Period of half a year Method: Impute missing values

Predictive Mean Matching (PMM) multiple imputation

Generalized Loglinear Modeling (GLM) - Poisson verdeling

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UNDERCOVERAGE PR

Number of Usual Residents 151 thousand missed by all registers 33,000 people in ER and CSR residing for longer than 12 months Missed by all three registers: 184 thousand Confidence interval: 149 to 222 thousand Registered in the PR: 16,638,805

Assumption: Everyone in PR has intention to stay for longer than 12 months

Total Usual Residents 16,822 thousand, of which .5 to 1.1% not registered in the PR

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CAPTURE-RECAPTURE METHODOLOGY

Goes by many names and many versions: Mark-Recapture Dual(/triple) system estimation At least 2 data sources (registers, survey’s, lists, etc.) Use of covariates very useful Loglinear modelling (Census vs Post Enumeration Survey (PES) at the ONS, UK) Item Response Theory (IRT; Census enumeration Italy, IStat) Rasch models (On Diabetes data by Fienberg 1999, USA) Bayesian modelling (Qaulity assessment for the Census in New Zealand)

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HOMELESS PEOPLE NETHERLANDS

Capture-recapture methodology Multiple registers: PR People without address who receive social support Dutch register on alcohol and drug users LADIS Method and results Capture recapture methodology Observed in all registers 5,169 Estimated not observed: 12, 598 Total 17,5 thousand homeless CI: 15,000 - 21,000

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HOMELESS PEOPLE NETHERLANDS

Repeated 2012 27,000 Half of them have a foreign background and 40 percent have a non-western background. Nearly half of all homeless are found in Amsterdam, Utrecht, The Hague and Rotterdam. https://www.cbs.nl/en-gb/news/2013/52/27-thousand-homeless-in-the- netherlands

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HIV PREVALENCE

HIV in children, 2003-2006. Capture-recapture methodology Three source Capture-recapture estimation

Mandatory HIV case reporting (DOVIH) The mandatory HIV case reporting (DOVIH), Laboratory based surveillance of HIV (LaboVIH)

Multiple imputation for missing heterogeneous catchability Loglinear modeling Registered: 216 new HIV diagnoses. 117 were estimated. The number of new HIV diagnoses in children was estimated at 387 (95%CI 271-503) during 2003-2006, among whom 60% were born abroad

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HIV PREVALENCE

Heraud, et al., 2012

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MULTIPLIER METHOD

Method: At least two data sources - Comprehensive register and a survey Use distribution of people in the survey on the registers Multiplier methods are user-friendly for their mathematical simplicity, the absence of linkage and are straightforward to use Example: Nr of Polish individuals in the NL. Assume everyone has equal chance of going to a hospital We go to a hospital and address every polish person and ask if they are in the PR

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MULTIPLIER METHOD

Table: Artificial observed data for the Polish people in the hospital

Hospital 1 GBA 1 150 39,850 40,000 50

  • 200
  • Example:

There are 200 Polish people, of which 150 are in the GBA. Thus p(GBA |Hospital) = 0.75 Total 40,000 Polish people registered in PR Then 40, 000/0.75 = 53, 333 and we missed 53, 333 − 40, 000 = 13, 333 people who are not registered in the PR.

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DRUG USERS

Multiplier method for harddrug users Sample: 572 problematic drug users were interviewed in 8 Dutch cities. These individuals were asked whether they were registered in LADIS, a Dutch registry on drug users Multiplier method on number of interviewed users were in LADIS Different multiplier for Sex, Age and drug used. Opiates: 17,700 (CI: 17,300 - 18,100) Crack: 12,400 (CI: 10,300 - 15,600) Cruts et al., 2010

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CASEFINDING

Method Casefinding is a system for locating every patient-inpatient or outpatient- who is diagnosed and/or treated with a reportable diagnosis. Casefinding is like casting a net far and wide to "capture" all of the reportable cancer cases. A combination of active and passive casefinding is a commonly used system in registries today. Active: When researchers or users investigate all source documents for possible matches Passive: When cases are brought to the attention of the registry holder.

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HOSPITAL REGISTRY

National Cancer Institute (USA) http://training.seer.cancer.gov/

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SURVIVAL ANALYSES

Survival analyses - Very broad methodology Data is used on a time period until a specific event occurs. Non parametric method: Kaplan Meier analysis Log-rank statistical test, Mantel-Haenszel, Comparing two or more genrations in time

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VACCINATION

Vaccination coverage in 3 regions in Slovenia Population: Hard-to-reach Roma Children The data were obtained from health records, immunization records and National Computerized Immunization System (CEPI 2000Â R ) Total of 436 preschool and 551 schoolchildren children Vaccination coverage was calculated by comparing the number of children eligible for immunization with the number of vaccinated children. Log-Rang test, compared the survival curves for the two generations.

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SURVIVAL ANALYSIS

Kraigher, et al. 2006.

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OTHER EXAMPLES

Other methods: Snowballing Respondent driven sampling Weighing Cumulative (Bakker, 2009) Which to choose? Depends on your data and resources

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Thank you!

Email: s.c.gerritse@uu.nl, sc.gerritse@cbs.nl Website: http://scgerritse.wordpress.com/