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Secondary Data Usage Osamah Hamouda, MD, MPH Dept. for Infectious Disease Epidemiology Robert Koch Institute, Berlin DFG Roundtable Discussion „Public Health Research in Germany“ January 20-21 2014, Bonn 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) 2 environmental safety Public Health Research in Germany 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 Data scources Primary data • National Surveillance data on reportable infectious diseases • Sentinel surveillance • Epidemiological research projects • National Health Surveys Secondary data 4 • Associations of Statutory Health Insurance Practitioners (ASHIPs, KVen) • Morbi RSA • billing data from pharmacies • CoD Statistics 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 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) Vaccination coverage Practitioner ASHIP Statutory health insured patients 85% pop. Practitioner RKI Practitioner ASHIP Practitioner Incidences and disease burden Vaccine effectiveness Measles vaccination coverage: estimates from retrospective cohort study Vaccination coverage [%] Cross-sectional, nationwide (Rieck et al. Human Vaccines & Immunotherapeutics 2014; 10:27- 26) Birth cohorts 2004-2009; Age:24 months / 36 months Measles vaccination coverage: estimates from retrospective cohort study Regional, districts in Baden-Württemberg Birth cohort 2008, Age: 24 months Vaccination coverage 2nd dose measles [%] (Rieck et al. Human Vaccines & Immunotherapeutics 2014; 10:27- 26) Measles vaccination coverage: estimates from retrospective cohort study Longitudinal, Schleswig-Holstein 1 dose 2 doses Birth cohorts 2004-2009 (Rieck et al. Human Vaccines & Immunotherapeutics 2014; 10:27- 26) 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 • • 15 Objective 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) • 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 Public Health Research in Germany Hantavirus infections Ecological Regression of Hantavirus Incidence, Germany 2002-2012* 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 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 SECONDARY ??? DATA NASHIP Data: Number of 2,964,346 CT tests CT oftests = 85.9% all CT tests in Germany in Germany PRIMARY DATA CT laboratory sentinel data: 1,126,073 CT tests 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. 20 Public Health Research in Germany Opportunistic CT-screening by age-group and time* *data as of 27.11.2012, for 2012 quartal 1 and 2 21 Public Health Research in Germany 21 Positivity rate by age-group and test reason 12% Opportunistic screening (n= 230,829) Positivity rate 10% Screening in pregnancy (n=432,604) Diagnostic test (n=286,748) 8% 6% 4% 2% 0% < 15 15<20 20<26 26<30 Age-group 22 Public Health Research in Germany 30<35 35<40 40+ Coverage of opportunistic CT-screening Opportunistic screening (n=231,568) 19.3% Diagnostic (n=289,863) 24.1% Screening in pregnancy (n=434,785) 36.2% 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) 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 Geschätzte Gesamtzahl der HIV-Neuinfektionen in Deutschland nach Infektionsjahr und Transmissionsrisiko 6000 HIV-NeuInfektionen bei MSM 5000 HIV-NeuInfektionen bei IVD HIV-NeuInfektionen bei Hetero (Inland) Anzahl 4000 3000 2000 1000 0 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 Maximum number in each stratum of sex and 5-year age group Estimated number of death with HIV stratified by sex, 5-years age group and transmission group (imputed with multiple imputation) 26 Public Health Research in Germany Estimated number of people living with HIV/AIDS in Germany 100.000 Anzahl 80.000 HIV-Prävalenz HIV-Prävalenz - Gesamt (untere Schranke) HIV-Prävalenz - Gesamt (obere Schranke) 60.000 40.000 20.000 0 (ohne Hämophile/ Transfusionsempfänger und perinatal infizierte Kinder) 27 Jahr Public Health Research in Germany Determine the number of persons receiving antiretroviral therapy (ART) in Germany using information on therapy regimes from ClinSurv data * Antiretroviral prescription data (APD) by pharmacy billing centres Number of persons receiving ART in Germany covering >99% of prescriptions of persons with statutory health insurance (SHI) * Additional adjustment for exotic ART regimes and ART interruptions necessary (Assumption: ClinSurv is representative) 28 Public Health Research in Germany Determine the number of persons receiving antiretroviral therapy (ART) in Germany 60000 100% 90% 50000 40000 80% 70% 60% 30000 50% SHI (TCM) 90 days SHI (TCM) 30 days Treated patients (seasonal adjusted) 40% 20000 10000 30% SHI (TCM) seasonal adjusted SHI (TCM) 20% 10% 0 • • • • 29 0% 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 Public Health Research in Germany Number of people living with HIV/AIDS in Germany and proportion diagnosed and under ART, 2001 - 2012 Anzahl Anteil 80.000 100% 80% 60.000 Unter Therapie Diagnostiziert, ohne Therapie Nicht Diagnostiziert 60% 40.000 40% 20.000 20% 0 0% Jahr 30 Public Health Research in Germany Anteil unter Therapie bezogen auf alle Diagnostizierten Anteil unter Therapie bezogen auf alle Infizierten Thanks for your attention Vielen Dank für Ihr Interesse 31