secondary data usage - dfg · – (1) non-official part of the death certificate contains cause of...

Osamah Hamouda, MD, MPH Dept. for Infectious Disease Epidemiology

Robert Koch Institute, Berlin

Secondary Data Usage

DFG Roundtable Discussion „Public Health Research in Germany“

January 20-21 2014, Bonn

2 Public Health Research in Germany

Federal Health Agencies

Ministry of Health

Robert Koch Institut (RKI) disease control and prevention

Paul Ehrlich Institut (PEI) licensing of vaccines and immunological medical products

Bundesamt für Arzneimittel und Medizinprodukte (BfArM) licensing of drugs and medical products

Bundeszentrale für gesundheitliche Aufklärung (BZgA) health promotion

Deutsches Institut für medizinische Dokumentation und Information (DIMDI) medical documentation and information

Ministry of Agriculture and Consumer Protection Friedrich Loeffler Institut (FLI) animal health

Bundesinstitut für Risikobewertung (BfR) risk assessment for food and other products

Ministry for the Environment, Nature Conservation and Nuclear Safety Umweltbundesamt (UBA) environmental safety

3

Objectives and mission

• Health Monitoring

• Surveillance of notifiable infectious disease

• Sentinel surveillance

• Epidemiological research projects • Guidance (recommendations) • Concepts for disease prevention

• Training programme in infectious disease epidemiology

• advisory committees

• European and International networks (ECDC, WHO) • 24 h / 7 d on-call duty service / assist in outbreak investigation


4

Data scources

Primary data • National Surveillance data on reportable infectious diseases • Sentinel surveillance • Epidemiological research projects • National Health Surveys

Secondary data • Associations of Statutory Health Insurance Practitioners

(ASHIPs, KVen) • Morbi RSA • billing data from pharmacies • CoD Statistics


Data on vaccination coverage and incidence of vaccine-preventable diseases

Background • annual school entry examinations at age 5-7 yrs are

only continuous and nationwide legally enforced source for vaccination coverage data

• not all vaccine-preventable diseases notifiable

• underreporting exists in the national passive surveillance system

• Evaluate the performance/impact of national immunization program


Research project: vaccination monitoring utilizing health insurance claims data

Statutory health insured patients

PractitionerASHIP

RKIPractitioner

Practitioner

PractitionerASHIP

Project partners: RKI and all 17 Associations of Statutory Health Insurance Practitioners (ASHIPs; KVen) funding BMG (2014) Utilization of ASHIP routine data (billing data of practitioners) Data covers ~85% of German population Data transfer: 500 million anonymized data sets per year (ID, vaccination codes, ICD-10

diagnosis codes, physician contact dates, sex, age, etc.) ASHIP-generated IDs ensure data protection but allow for cohort analysis

(consolidation of vaccinations and diagnoses to single-patient level)

85% pop.

Vaccination coverage

Incidences and disease burden

Vaccine effectiveness

Measles vaccination coverage: estimates from retrospective cohort study

Birth cohorts 2004-2009; Age:24 months / 36 months

Vacc

inat

ion

cove

rage

[%]

Cross-sectional, nationwide

(Rieck et al. Human Vaccines & Immunotherapeutics 2014; 10:27- 26)


Vaccination coverage 2nd dose measles [%]

Birth cohort 2008, Age: 24 months


Regional, districts in Baden-Württemberg



Longitudinal, Schleswig-Holstein

Birth cohorts 2004-2009

1 dose 2 doses

Cattle density and STEC incidence in Germany

Background

• In Germany, ~1,000 human Shiga Toxin-producing E. coli (STEC) cases notified annually

• Large differences in STEC incidence by geographic region.

• Cattle important reservoir für STEC O157

• Spatial ecological studies: geographic variation in disease risk and its association with

explanatory variables measured at spatial unit level (eg, districts).

Objective Is there an association between cattle density and STEC incidence among humans for – all STEC serogroups combined? – specific serogroups?



Methods Data sources • National surveillance data

– STEC cases 2001-2003, n = 2,280 – Incidences by district

• Data on cattle density (National database) (Herkunftssicherungs- und Informationssystem für Tiere, HIT)

– Heads of cattle per km2 by district

Statistical methods: Poisson regression – Modelling of STEC incidence considering:

cattle density, district, age group, week of notification, seasonal variation

Frank C, Kapfhammer S, Werber D, Stark K, Held L. Cattle density and Shiga toxin-producing Escherichia coli infection in Germany: increased risk for most but not all serogroups. Vector Borne Zoonotic Dis. 2008 Oct;8(5):635-43.

Distribution of STEC incidence and cattle density in Germany

STEC incidence Cattle density

Frank C, et al. Vector Borne Zoonotic Dis. 2008 12 Public Health Research in Germany


Results and discussion

• Living in districts with higher cattle density significantly increased the STEC risk.

Risk increased by 68% per 100 cattle / km2

• This applies to all serogroups analysed (except for O91: different risk factors?).

• The results are stable in different models.

• Secondary data in combination with surveillance data are useful for modelling.

• No conclusions possible regarding risk factors on the individual level.

• However, important information about the impact of direct and indirect contact to cattle in STEC transmission.


Hantavirus infections: Background and Epidemiology

• Hantaviruses are rodent borne hemorrhagic fever viruses with a world wide distribution. Human infection through inhalation of virus particles.

• High spatio-temporal variability of disease incidence due to fluctuations in abundance of the reservoir animals.

Hantavirus infections Ecological Regression of Hantavirus

Incidence, Germany 2002-2012*

Data sources • dependent variable:

Hantavirus incidence (RKI surveillance data)

• independent variables: – Tree species distribution - Federal

Forest Inventory data - Thünen Institute

– Yearly tree fructification data - Forest condition surveys – Thünen institue

– CORINE land cover raster data – German Aerospace Centre (DLR)

– State and county borders - Federal Agency for Cartography and Geodesy (BKG)

Objective • quantify influence of

environmental factors on space time distribution of HTV infections to aid in outbreak prediction

Methods • Generalized additive model

containing a 2D-spline to smooth point-samples to county values.

• Ecological regression, Bayesian model with spatial effects (Schrödle and Held, 2011, 2010)

*Faber, Stark, Höhle (RKI), Hilbrig, Polley (Thünen Institute), unpublished 15 Public Health Research in Germany

Preliminary results and conclusions • Strong predictors of hantavirus incidence:

– proportion of county area covered by beech forest – proportion of beech trees showing medium or strong fructification in the previous

year

• Possible opportunity for: – prediction of hantavirus outbreaks in space and time

– targeted recommendations to the public

– prevention

Hantavirus infections Ecological Regression of Hantavirus

Incidence, Germany 2002-2012*


Secondary analysis of vital registration data • Vital registration statistics

– (1) non-official part of the death certificate contains cause of death (primary data use: stats about most frequent cause of death by age, …)

– (2) official part of the death certificate contains age, sex, week of death. Primary data use: calculation and forecasting of demographic indicators

• (1) suitable for retrospective estimation of deaths associated with influenza (e.g. through modeling of all respiratory deaths)

• (2) suitable for real-time monitoring of excess deaths, relevant e.g. in the case of influenza pandemics or bioterror attacks

(1)

(2)

Chlamydia trachomatis (CT) infection

• Most frequent sexually transmitted infection

• Often asymptomatic (up to 80% females and 50% males) • Serious long-term complications possible

– Pelvic inflammatory disease (PID) in 5-40%: tubal blockage infertility and ectopic pregnancies – Epididymitis with infertility

• Screening for women < 25 yrs introduced in 2008 G-BA

• Not reportable (exept Sachsen)


Analysis of the Chlamydia trachomatis laboratory sentinel

Background Evaluation of representativeness of the Chlamydia trachomatis (CT) laboratory sentinel using CT accounting data of the National Association of Statutory Health Insurance Physicians (Kassenärztliche Bundesvereinigung (KBV); NASHIP) Approach • Primary data: CT tests within the laboratory sentinel • Secondary data: NASHIP data of accounted CT tests of patients with

statutory health insurance Aim • Calculate the percentages of all CT tests conducted in Germany

detected by the laboratory sentinel • Determine coverage of CT Screening


Results of the secondary data analysis

Data weighting:

Sentinel data * 0.859 / NASHIP data = 32.6% Coverage

Regional coverage: Good coverage (over 20%) in most of the federal states (11 of 16) Poor coverage (under 10%) in only 1 federal state (Baden-Württemberg)

Gender specific coverage: better coverage of women (42.9%) than of men (23.4%)

Results were used to develop a recruitment plan for further CT data collection.

??? Number of CT tests

in Germany

SECONDARY DATA NASHIP Data:

2,964,346 CT tests = 85.9% of all CT tests

in Germany

PRIMARY DATA CT laboratory sentinel data: 1,126,073 CT

tests


Opportunistic CT-screening by age-group and time*

21 *data as of 27.11.2012, for 2012 quartal 1 and 2 21 Public Health Research in Germany

Positivity rate by age-group and test reason

0%

2%

4%

6%

8%

10%

12%

< 15 15<20 20<26 26<30 30<35 35<40 40+

Age-group

Posi

tivi

ty ra

te

Opportunistic screening (n= 230,829)

Screening in pregnancy (n=432,604)

Diagnostic test (n=286,748)


Coverage of opportunistic CT-screening

In general population*: 7 women 15-25 years old per 1 pregnant women

CT sentinel data: 0.5 women 15-25 years old (opportunistic screening) per 1 pregnant women (screening in pregnancy)

→ Opportunistic screening coverage 8%

* 4,500,000 women 15-25 years old (2011), 665,000 Pregnant women (2011)

Opportunistic screening (n=231,568) 19.3%

Screening in pregnancy (n=434,785) 36.2%

Diagnostic (n=289,863) 24.1%


Estimating HIV Prevalence and number of PLWHA under ART

Background • Measuring HIV incidence and prevalence is difficult • Important for guiding public Health decision making • Targeting prevention and health services

Approach • Estimate HIV incidence, prevalence and number of PLWHA under

ART using – surveillance data – mortality statistics data – antiretroviral therapy billing data from pharmacies


0

1000

2000

3000

4000

5000

6000HIV-NeuInfektionen bei MSM

HIV-NeuInfektionen bei IVD

HIV-NeuInfektionen bei Hetero (Inland)

Geschätzte Gesamtzahl der HIV-Neuinfektionen in Deutschland nach Infektionsjahr und

Transmissionsrisiko

Anza

hl

Jahr der Infektion

Estimating the number of death with HIV using causes of death statistics

Death with HIV from causes of death statistics stratified by sex and 5-years

age group

Death with HIV from AIDS case register

stratified by sex, 5-years age group and transmission group

Estimated number of death with HIV

stratified by sex, 5-years age group and transmission group (imputed with

multiple imputation)

Maximum number in each stratum of

sex and 5-year age group


Estimated number of people living with HIV/AIDS in Germany

0

20.000

40.000

60.000

80.000

100.000HIV-Prävalenz

HIV-Prävalenz - Gesamt (untere Schranke)

HIV-Prävalenz - Gesamt (obere Schranke)

Anza

hl

Jahr (ohne Hämophile/ Transfusionsempfänger und perinatal infizierte Kinder)


Determine the number of persons receiving antiretroviral therapy (ART) in Germany

Antiretroviral prescription data (APD) by pharmacy billing centres covering >99% of prescriptions of persons with statutory health insurance (SHI)

using information on therapy regimes from ClinSurv data *

* Additional adjustment for exotic ART regimes and ART interruptions necessary (Assumption: ClinSurv is representative)

Number of persons receiving ART in Germany


Determine the number of persons receiving antiretroviral therapy (ART) in Germany

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

10000

20000

30000

40000

50000

60000

SHI (TCM) 90 days

SHI (TCM) 30 days

Treated patients (seasonal adjusted)

SHI (TCM) seasonal adjusted

SHI (TCM)

• Number of persons with statutory health insurance (SHI) receiving ART SHI (TCM) • Due to seasonal fluctuations in prescriptions SHI (TCM) were seasonal adjusted • All persons receiving ART in Germany (including non-TCM, interruptions and non-SHI) • Prescribed formulations changed over time, packages for 90 days increased


0%

20%

40%

60%

80%

100%

0

20.000

40.000

60.000

80.000Unter Therapie

Diagnostiziert, ohneTherapie

Nicht Diagnostiziert

Anteil unter Therapiebezogen auf alleDiagnostiziertenAnteil unter Therapiebezogen auf alleInfizierten

Number of people living with HIV/AIDS in Germany and proportion diagnosed and under ART, 2001 - 2012

Anteil

Jahr

Anzahl


Thanks for your attention

Vielen Dank für Ihr Interesse

31

secondary data usage - dfg · – (1) non-official part of the death certificate contains cause of...

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