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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
3 Public Health Research in Germany
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
4 Public Health Research in Germany
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
5 Public Health Research in Germany
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)
Measles vaccination coverage: estimates from retrospective cohort study
Vaccination coverage 2nd dose measles [%]
Birth cohort 2008, Age: 24 months
(Rieck et al. Human Vaccines & Immunotherapeutics 2014; 10:27- 26)
Regional, districts in Baden-Württemberg
Measles vaccination coverage: estimates from retrospective cohort study
(Rieck et al. Human Vaccines & Immunotherapeutics 2014; 10:27- 26)
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?
10 Public Health Research in Germany
Cattle density and STEC incidence in Germany
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
Cattle density and STEC incidence 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.
13 Public Health Research in Germany
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*
16 Public Health Research in Germany
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)
18 Public Health Research in Germany
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
19 Public Health Research in Germany
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
20 Public Health Research in Germany
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)
22 Public Health Research in Germany
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%
23 Public Health Research in Germany
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
24 Public Health Research in Germany
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
26 Public Health Research in Germany
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)
27 Public Health Research in Germany
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
28 Public Health Research 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
29 Public Health Research in Germany
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
30 Public Health Research in Germany
Thanks for your attention
Vielen Dank für Ihr Interesse
31