Aníbal García Sempere, Área de investigación en Servicios de Salud y Farmacoepidemiología. FISABIO|SaludPública, Valencia

1. Real World Data en el análisis
de la
utilización, seguridad y
efectividad de medicamentos
Salvador Peiró
Isabel Hurtado
Gabriel Sanfélix‐Gimeno
Clara Rodriguez‐Bernal
Aníbal García‐Sempere
Inv. Serv. Salud, FISABIO, Valencia
Redissec

2. Big Data, Real World Data, Clinical Trial Data
 Big Data: gigantic and heterogeneous volumes of data that cannot be managed
with traditional software and hardware, nor analysed with conventional data
management tools.
 In healthcare, we are today specially interested in a relatively small portion of
this Big Data consisting mostly of structured (and semistructured) information
generated during the provision of healthcare, which we call Real World Data
(RWD).
 In contrast with clinical trials, in RWD studies:
 Real patients vs strict inclusion and exclusion criteria
 Real conditions vs experimental
 Older populations, comorbid patients, pregnant women, children/young
people
 Low incidence events / events ocurring in the long term
 Speed / cost

3. Raw materials: routinely collected, population‐based data
Integrated Information Systems allow the follow‐up of large cohorts
Osteoporosis
11,000
Hip Fracture
20,000
Ischemic Disease
16,500
Atrial Fibrillation
45,000
Asthma
20,000
Opioids
150,000
N
E
W
N
E
W
Cohorts (n)

4. How can RWD help improve clinical practice?
Miguel A. Hernán, John Hsu & Brian Healy (2019) A Second Chance to Get Causal
Inference Right: A Classification of Data Science Tasks, CHANCE, 32:1, 42-49, DOI:
10.1080/09332480.2019.1579578

5. How can RWD help improve clinical practice?
 RWD may help identifying effective, safe and cost‐effective interventions,
technologies and pharmaceuticals: effectiveness and safety studies,
comparative effectiveness studies, impact of interventions studies.
 RWD may help in the provision of effective healthcare to optimal
candidates: stratification, predictive modelling, appropiateness studies.
 RWD may help in the assessment of the quality, safety and efficiency of
healthcare organisations and in the prevention of mis‐spending: patterns
of use, medication adherence, cost‐effectiveness.
 RWD may help to identify inequalities / variations in the healthcare we
provide to different patient groups (gender, age, socioeconomic status,
nationality, place of residence): patterns of use, multilevel analyses,
variations studies.

6. Patterns of initiation / variations: what is the treatment of
choice to start anticoagulation in AF patients?
Vitamin K antagonists vs DOAC
nov2011-feb2014:totalinitiating21.879patients

7. Patterns of initiation / variations: what is the treatment of
choice to start anticoagulation in AF patients?
VKA
García-Sempere A, Bejarano-Quisoboni D, Librero J, Rodríguez-Bernal CL, Peiró S,
Sanfélix-Gimeno G. A Multilevel Analysis of Real-World Variations in Oral Anticoagulation
Initiation for Atrial Fibrillation in Valencia, a European Region. Front Pharmacol. 2017 Aug
24;8:576.

8. Patterns of management / variations: which treatment for
osteoporotic hip fracture patients?
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
PTH
Denosumab
Ranelato
Calcitoni
Raloxifen
Bifosfona
• Bisph: [47,7% - 81,7%]
• PTH: [4,2% - 23,7%]
• Denosumab: [0,8% - 11,3%]
• 2008-2015
• Hip fracture patients: 30.145
• Treated patients in the three months
after discharge

9. Appropiateness: are we treating women at a high risk of
osteoporosis‐related fractures?
Sanfélix-Gimeno G, Hurtado I, Sanfélix-Genovés J, Baixauli-Pérez C,
Rodríguez-Bernal CL, Peiró S. Overuse and Underuse of Antiosteoporotic
Treatments According to Highly Influential Osteoporosis Guidelines: A
Population-Based Cross-Sectional Study in Spain. PLoS One.
2015;10(8):e0135475.
• Variability among CPG
recommendations
• 46% of women treated with
osteporosis drugs in Valencia had
no treatment recommendation
from any Clinical Practice
Guideline (61% when considering
international CPGs)
• 3,4% of non-treated women
require treatment according to all
CPGs

10. Appropiateness: are we treating patients after an
osteoporotic hip fracture?
• 2008-2015
• Hip fracture patients: 30.145
• % of patients treated in the 3
months following discharge
• From 28,9% in 2009 to 16,4% in 2015
• High variability between Dpts.
• 20/20 reduce use
• In 9/24 more than 50% reduction

11.  Los RWD no proceden de ensayos clínicos con procesos predefinidos, definiciones
operativas de los endpoint, y seguimiento estricto.
 Sesgo de información: errores que se introducen durante la medición de la exposición,
de los eventos u otras covariables en la población en estudio.
 Sesgo de selección: se produce cuando la asociación entre la exposición y el evento
incluye un componente no causal atribuible a haber restringido el análisis a un(os)
cierto(s) nivel(es) de un efecto común a la exposición y el evento, o, en términos
generales, a haber condicionado el análisis a un efecto común de variables
correlacionadas con la exposición y el evento.
 Confusión: ocurre cuando la relación entre exposición y evento incluye un componente
no‐causal atribuible a la existencia de una causa común no medible.
• Confusor: se trata de una variable asociada con la exposición en la población,
asociada con el evento de forma condicional a la exposición, y que no está en el
trayecto causal entre exposición y evento.
• Ej: la edad (o la cantidad de ingesta) es un confusor en la relación entre ejercicio
físico y obesidad. En la relación entre hipertensión e ictus, fumar o beber alcohol
serían confusores.
El análisis de los datos de vida real no es sencillo y es
altamente manipulable

12. Measured
confounding
Unmeasured
confounding
Design Analysis Unmeasured,
but measurable
in substudy
Unmeasurable
 Instrumental
variable
 Proxy
analysis
 Sensitivity
analysis
Design Analysis
 Cross‐over
 Active
comparator
(restriction)
 2‐stage sampl.
 Ext.
adjustment
 Imputation
 Restriction
 Matching
 Standardization
 Stratification
 Regression
 Marginal structural
models
 Propensity scores
There is a set of methods and designs to try to control the
effect of different kinds of confounding
.. and expert clinical knowledge is as
important as methods
Adaptado de: Schneeweiss S. Sensitivity analysis and
external adjustment for unmeasured confounders in
epidemiologic database studies of therapeutics.
Pharmacoepidemiol Drug Saf. 2006 May;15(5):291-303.

13. Effectiveness: how effective are (osteoporosis) treatments
in hip fracture patients in the real world?
Medication
No medication
PDC<80%
No medication
PDC>80%
• Multivariable Cox model
• Fine and Gray: competitive risk of
death
• Instrumental variables (instrument:
Health Dpt)
IV ANALYSIS• 24.000 patients (2009-2012)

14. Comparative effectiveness: what are the real‐world results of
treatments for stroke prevention in AF patients?
41,560
AF patients initiating
anticoagulation therapy
between nov 11 dec 15
VKA
32,450
Apixaban
2,245
Dabigatran
3,366
Rivaroxaban
3,449
Mean follow up:
1,8 years
New user design
IPTW
Stroke TIA Mortality
GI
bleeding
Major GI
bleeding
ICH

15. Acenocumarol
n=32,476
Apixaban
n=2,259
Dabigatran
n=3,380
Rivaroxaban
n=3,445
Standardized
Differences*
Before After
Age; mean (SD) 74.82 (9.58) 75.03 (10.67) 72.30 (11.30) 74.72 (10.63) ‐ ‐
Female 15,464 (47.62) 1,086 (48.05) 1,467 (43.40) 1,642 (47.66) 0.09 0.02
CHADS2 score†; mean (SD) 2.20 (1.24) 2.31 (1.39) 1.99 (1.37) 2.14 (1.32) ‐ ‐
CHA2DS2‐VASC score‡; mean (SD) 3.83 (1.65) 3.89 (1.77) 3.39 (1.86) 3.71 (1.78) ‐ ‐
HAS BLED score§; mean (SD) 2.93 (1.17) 3.04 (1.25) 2.74 (1.23) 2.89 (1.22) ‐ ‐
Diagnosis
Atrial Fibrilation 30,537 (94.03) 2,150 (95.17) 3,183 (94.17) 3,274 (95.04) 0.05 0.01
Atrial Flutter 1,939 (5.97) 109 (4.83) 197 (5.83) 171 (4.96) ‐ ‐
Comorbidities
Congestive heart failure 6,027 (18.56) 431 (19.08) 441 (13.05) 561 (16.28) 0.15 0.01
Hypertension 26,149 (80.51) 1,796 (79.50) 2,511 (74.29) 2,704 (78.49) 0.15 0.04
Diabetes 11,702 (36.03) 758 (33.55) 989 (29.26) 1,121 (32.54) 0.14 0.04
Liver disease 2,397 (7.38) 179 (7.92) 224 (6.63) 240 (6.97) 0.03 0.01
Renal disease 4,555 (14.03) 280 (12.39) 206 (6.10) 390 (11.32) 0.27 0.02
COPD 3,959 (12.19) 241 (10.67) 328 (9.70) 339 (9.84) 0.08 0.02
Previous ischemic stroke or TIA 4,411 (13.58) 490 (21.69) 583 (17.25) 534 (15.50) 0.21 0.03
Dementia 2,205 (6.79) 214 (9.47) 212 (6.28) 302 (8.77) 0.10 0.02
Depression 4,464 (13.75) 326 (14.43) 396 (11.72) 503 (14.60) 0.06 0.01
Cancer 4,810 (14.81) 347 (15.36) 393 (11.63) 483 (14.02) 0.09 <0.01
Coronary disease 6,061 (18.66) 424 (18.77) 524 (15.50) 594 (17.24) 0.08 0.02
Thromboembolism 2,462 (7.581) 116 (5.13) 137 (4.05) 224 (6.50) 0.15 0.01
Intracraneal haemorrage 199 (0.61) 48 (2.13) 62 (1.83) 34 (0.99) 0.13 0.01
Gastrointestinal bleeding 1,404 (4.32) 119 (5.27) 117 (3.46) 146 (4.24) 0.04 0.04
Other bleeding 7,668 (23.61) 581 (25.72) 656 (19.41) 757 (21.97) 0.10 0.01
Medication use
NSAIDs 5,964 (18.36) 385 (17.04) 702 (20.77) 590 (17.13) 0.06 <0.01
ASA 11,971 (36.86) 941 (41.66) 1,462 (43.25) 1,470 (42.67) 0.13 <0.01
Clopidogrel 1,402 (4.32) 122 (5.40) 177 (5.24) 175 (5.08) 0.05 <0.01
ASA+Clopidogrel 1,425 (4.39) 87 (3.85) 137 (4.05) 136 (3.95) 0.03 0.02
Other antiplatelets 1,022 (3.15) 64 (2.83) 109 (3.23) 103 (2.990) ‐ ‐
IPTW

16. Comparative effectiveness: what are the real‐world results
treatments for stroke prevention in AF patients?
Main analysis
Apixaban
Dabigatran
Rivaroxaban
Atrial Fibrilation
Apixaban
Dabigatran
Rivaroxaban
75
Apixaban
Dabigatran
Rivaroxaban
Male
Apixaban
Dabigatran
Rivaroxaban
Female
Apixaban
Dabigatran
Rivaroxaban
CHA2DS2-VASC 2
Apixaban
Dabigatran
Rivaroxaban
HAS-BLED 3
Apixaban
Dabigatran
Rivaroxaban
1.35 (0.96, 1.91)
1.03 (0.81, 1.31)
1.03 (0.78, 1.35)
1.38 (0.98, 1.96)
1.02 (0.80, 1.31)
1.01 (0.77, 1.33)
1.68 (1.14, 2.46)
1.08 (0.80, 1.44)
1.02 (0.73, 1.41)
1.32 (0.82, 2.14)
1.10 (0.80, 1.52)
1.08 (0.74, 1.58)
1.40 (0.85, 2.30)
0.94 (0.65, 1.35)
1.00 (0.68, 1.47)
1.56 (1.10, 2.22)
1.03 (0.78, 1.34)
0.97 (0.72, 1.32)
1.45 (0.99, 2.11)
1.04 (0.79, 1.37)
1.00 (0.73, 1.37)
1.35 (0.96, 1.91)
1.03 (0.81, 1.31)
1.03 (0.78, 1.35)
1.38 (0.98, 1.96)
1.02 (0.80, 1.31)
1.01 (0.77, 1.33)
1.68 (1.14, 2.46)
1.08 (0.80, 1.44)
1.02 (0.73, 1.41)
1.32 (0.82, 2.14)
1.10 (0.80, 1.52)
1.08 (0.74, 1.58)
1.40 (0.85, 2.30)
0.94 (0.65, 1.35)
1.00 (0.68, 1.47)
1.56 (1.10, 2.22)
1.03 (0.78, 1.34)
0.97 (0.72, 1.32)
1.45 (0.99, 2.11)
1.04 (0.79, 1.37)
1.00 (0.73, 1.37)
10.25 0.5 1 1.5 2.5
Ischemic stroke
1.20 (0.56, 2.58)
1.05 (0.62, 1.78)
0.70 (0.35, 1.38)
1.22 (0.57, 2.62)
0.93 (0.53, 1.62)
0.71 (0.36, 1.39)
1.28 (0.52, 3.18)
1.12 (0.61, 2.06)
0.86 (0.42, 1.78)
0.97 (0.31, 3.10)
0.84 (0.38, 1.88)
0.32 (0.08, 1.32)
1.34 (0.48, 3.72)
1.31 (0.64, 2.67)
1.06 (0.48, 2.34)
1.47 (0.68, 3.17)
1.23 (0.71, 2.14)
0.75 (0.36, 1.54)
1.34 (0.59, 3.07)
1.14 (0.64, 2.06)
0.74 (0.34, 1.60)
1.20 (0.56, 2.58)
1.05 (0.62, 1.78)
0.70 (0.35, 1.38)
1.22 (0.57, 2.62)
0.93 (0.53, 1.62)
0.71 (0.36, 1.39)
1.28 (0.52, 3.18)
1.12 (0.61, 2.06)
0.86 (0.42, 1.78)
0.97 (0.31, 3.10)
0.84 (0.38, 1.88)
0.32 (0.08, 1.32)
1.34 (0.48, 3.72)
1.31 (0.64, 2.67)
1.06 (0.48, 2.34)
1.47 (0.68, 3.17)
1.23 (0.71, 2.14)
0.75 (0.36, 1.54)
1.34 (0.59, 3.07)
1.14 (0.64, 2.06)
0.74 (0.34, 1.60)
10.25 0.5 1 1.5 2.5
TIA
0.92 (0.78, 1.08)
0.91 (0.82, 1.00)
1.02 (0.92, 1.14)
0.93 (0.79, 1.09)
0.90 (0.81, 0.99)
1.02 (0.92, 1.14)
0.88 (0.74, 1.05)
0.94 (0.84, 1.05)
1.07 (0.96, 1.20)
0.90 (0.71, 1.12)
0.85 (0.74, 0.98)
1.03 (0.89, 1.19)
0.92 (0.73, 1.16)
0.98 (0.84, 1.13)
1.01 (0.87, 1.18)
0.93 (0.78, 1.10)
0.94 (0.85, 1.05)
1.07 (0.96, 1.20)
0.92 (0.77, 1.09)
0.93 (0.83, 1.04)
1.01 (0.90, 1.14)
0.92 (0.78, 1.08)
0.91 (0.82, 1.00)
1.02 (0.92, 1.14)
0.93 (0.79, 1.09)
0.90 (0.81, 0.99)
1.02 (0.92, 1.14)
0.88 (0.74, 1.05)
0.94 (0.84, 1.05)
1.07 (0.96, 1.20)
0.90 (0.71, 1.12)
0.85 (0.74, 0.98)
1.03 (0.89, 1.19)
0.92 (0.73, 1.16)
0.98 (0.84, 1.13)
1.01 (0.87, 1.18)
0.93 (0.78, 1.10)
0.94 (0.85, 1.05)
1.07 (0.96, 1.20)
0.92 (0.77, 1.09)
0.93 (0.83, 1.04)
1.01 (0.90, 1.14)
10.25 0.5 1 1.5 2.5
Death
HR (95% CI) HR (95% CI) HR (95% CI)
Favours
alternative
Favours
acenocoumarol
Favours
alternative
Favours
acenocoumarol
Favours
alternative
Favours
acenocoumarol

17. Main analysis
Apixaban
Dabigatran
Rivaroxaban
Atrial Fibrilation
Apixaban
Dabigatran
Rivaroxaban
75
Apixaban
Dabigatran
Rivaroxaban
Male
Apixaban
Dabigatran
Rivaroxaban
Female
Apixaban
Dabigatran
Rivaroxaban
CHA2DS2-VASC 2
Apixaban
Dabigatran
Rivaroxaban
HAS-BLED 3
Apixaban
Dabigatran
Rivaroxaban
0.56 (0.31, 1.00)
1.18 (0.90, 1.56)
0.94 (0.67, 1.30)
0.59 (0.33, 1.05)
1.22 (0.92, 1.61)
0.98 (0.70, 1.36)
0.41 (0.18, 0.92)
1.26 (0.90, 1.76)
1.07 (0.74, 1.55)
0.53 (0.23, 1.21)
1.35 (0.95, 1.92)
0.93 (0.59, 1.47)
0.58 (0.26, 1.33)
0.99 (0.63, 1.55)
0.98 (0.61, 1.58)
0.53 (0.27, 1.03)
1.22 (0.89, 1.67)
1.08 (0.77, 1.53)
0.65 (0.35, 1.19)
1.11 (0.80, 1.55)
1.01 (0.70, 1.45)
0.56 (0.31, 1.00)
1.18 (0.90, 1.56)
0.94 (0.67, 1.30)
0.59 (0.33, 1.05)
1.22 (0.92, 1.61)
0.98 (0.70, 1.36)
0.41 (0.18, 0.92)
1.26 (0.90, 1.76)
1.07 (0.74, 1.55)
0.53 (0.23, 1.21)
1.35 (0.95, 1.92)
0.93 (0.59, 1.47)
0.58 (0.26, 1.33)
0.99 (0.63, 1.55)
0.98 (0.61, 1.58)
0.53 (0.27, 1.03)
1.22 (0.89, 1.67)
1.08 (0.77, 1.53)
0.65 (0.35, 1.19)
1.11 (0.80, 1.55)
1.01 (0.70, 1.45)
GI bleeding
0.44 (0.18, 1.07)
0.91 (0.59, 1.42)
0.82 (0.50, 1.32)
0.46 (0.19, 1.11)
0.91 (0.58, 1.44)
0.86 (0.53, 1.39)
0.48 (0.18, 1.31)
1.20 (0.74, 1.96)
0.94 (0.55, 1.61)
0.35 (0.09, 1.44)
0.78 (0.40, 1.50)
0.94 (0.49, 1.79)
0.49 (0.15, 1.57)
1.08 (0.59, 1.99)
0.73 (0.35, 1.50)
0.53 (0.22, 1.28)
1.10 (0.70, 1.75)
0.89 (0.53, 1.49)
0.53 (0.22, 1.28)
0.87 (0.52, 1.46)
0.87 (0.51, 1.47)
0.44 (0.18, 1.07)
0.91 (0.59, 1.42)
0.82 (0.50, 1.32)
0.46 (0.19, 1.11)
0.91 (0.58, 1.44)
0.86 (0.53, 1.39)
0.48 (0.18, 1.31)
1.20 (0.74, 1.96)
0.94 (0.55, 1.61)
0.35 (0.09, 1.44)
0.78 (0.40, 1.50)
0.94 (0.49, 1.79)
0.49 (0.15, 1.57)
1.08 (0.59, 1.99)
0.73 (0.35, 1.50)
0.53 (0.22, 1.28)
1.10 (0.70, 1.75)
0.89 (0.53, 1.49)
0.53 (0.22, 1.28)
0.87 (0.52, 1.46)
0.87 (0.51, 1.47)
Major GI bleeding
0.60 (0.32, 1.12)
0.34 (0.20, 0.56)
0.55 (0.35, 0.86)
0.62 (0.33, 1.17)
0.33 (0.20, 0.55)
0.54 (0.34, 0.85)
0.56 (0.26, 1.19)
0.38 (0.22, 0.66)
0.52 (0.31, 0.88)
0.68 (0.30, 1.55)
0.44 (0.24, 0.82)
0.78 (0.46, 1.32)
0.51 (0.19, 1.37)
0.21 (0.09, 0.51)
0.32 (0.14, 0.72)
0.63 (0.33, 1.23)
0.36 (0.21, 0.62)
0.50 (0.30, 0.82)
0.59 (0.29, 1.19)
0.37 (0.21, 0.64)
0.55 (0.33, 0.91)
0.60 (0.32, 1.12)
0.34 (0.20, 0.56)
0.55 (0.35, 0.86)
0.62 (0.33, 1.17)
0.33 (0.20, 0.55)
0.54 (0.34, 0.85)
0.56 (0.26, 1.19)
0.38 (0.22, 0.66)
0.52 (0.31, 0.88)
0.68 (0.30, 1.55)
0.44 (0.24, 0.82)
0.78 (0.46, 1.32)
0.51 (0.19, 1.37)
0.21 (0.09, 0.51)
0.32 (0.14, 0.72)
0.63 (0.33, 1.23)
0.36 (0.21, 0.62)
0.50 (0.30, 0.82)
0.59 (0.29, 1.19)
0.37 (0.21, 0.64)
0.55 (0.33, 0.91)
Intracranial hemorrhage
10.25 0.5 1 1.5 2.5 10.25 0.5 1 1.5 2.5 10.25 0.5 1 1.5 2.5
Favours
alternative
Favours
acenocoumarol
Favours
alternative
Favours
acenocoumarol
Favours
alternative
Favours
acenocoumarol
HR (95% CI) HR (95% CI) HR (95% CI)
And safety results

18. Comparative effectiveness and safety: careful
interpretation
 We found no significant differences in the effectiveness and safety
profiles of NOAC compared to acenocoumarol.
 Results were consistent in subgroup analysis.
 However, we did find certain differences:
 Dabigatran and rivaroxaban: lower risk of
intracranial haemorrhage as compared to
acenocoumarol
 Apixaban: higher risk of ischemic stroke in
high risk patients (older and with higher
CHA2DS2‐VASC score), but lower risk of
gastrointestinal bleeding in older patients
 Dabigatran: decreased risk of death among
male patients
• Quite consistent result in main
and subanalyses
• “one time” result and it is a
subanalysis.
• Could be due to unmeasured
confounding (f.i. apixaban
patients were globally of a higher
risk, dabigatran patients were
healthier and younger)
• Cautious interpretation

19. Impact of interventions: what is the impact of cost sharing
on adherence to essential medication in high risk patients?
• El tratamiento
combinado con
antiagregantes, IECA
(o ARA II), beta-
bloqueantes y
estatinas permitiría
reducir en 2/3 los
casos de nuevo
infarto de miocardio
en los 10 años
siguientes a un
infarto previo.

20. Impact of interventions: what is the impact of cost sharing
on adherence to essential medication in high risk patients?
• Cohorte de todos los >18 años dados de alta vivos tras SCA en
2009-2011 (reclutamiento) que fueron seguidos hasta diciembre
2013 (18m tras RD).
• Cambios en el copago (RD 16/2012; vigencia: 1/7/2012).
• Experimento natural:
• Activos <18.000: sin cambios (copago 40%, sin topes)
• Activos > 18.000: de 40% a 50%, sin topes (60% si >100.000)
• Pensionistas: de exención a 10% con topes según renta
• 2 grupos intervención y 1 grupo control. Exclusión de pacientes que
cambian de grupo en el periodo.

21. Impact of interventions: what is the impact of cost sharing
on adherence to essential medication in high risk patients?
• Series semanales de adherencia (prescritas – dispensadas) a 4 grupos
de medicamentos de efectividad demostrada: Antiagregantes (excepto
clopidogrel), Betabloqueantes, IECA/ARA2, Estatinas.
• Betabloqueantes e IECA/ARA2 tienen cícero (tope de 2,57 o 4,50 por
envase) , pero no los antiagregantes, estatinas o las combinaciones a
dosis fijas de IECA o ARA2 con otros fármacos.
• Modelos de diferencias en diferencias (DiD) mediante regresión
segmentada para las tasas semanales de no adherencia. Comparan
ambos GI con el GC, e incluyen efectos inmediatos del cambio de
copago (variables de escalón) y cambios de tendencia.

22. Impact of interventions: what is the impact of cost sharing
on adherence to essential medication in high risk patients?

23. Impact of interventions: what is the impact of cost sharing
on adherence to essential medication in high risk patients?
 The cost‐sharing change had an immediate effect on the proportion of
adherence for angiotensin‐converting enzyme inhibitors and angiotensin
receptor blockers and for statins, in the pensioner group as compared with the
control group (6.8% and 8.3% decrease of adherence respectively, p<0.01 for
both).
 For the middle‐to‐high income group as compared to the control group only
adherence to statins significantly decreased after the reform (7.8% decrease in
adherence, p<0.01).
 No effect was found for low priced essential medications and low patient
maximum coinsurance (such as antiplatelet and beta‐blockers) in either
intervention group as compared with the control group.
 Reducing financial barriers to evidence‐based medication use could improve
health outcomes while reducing total cost of care. Accordingly, consideration
should be given to fully exempting high‐risk patients, as are patients after an
acute coronary syndrome, from drug cost‐sharing.

24. Impact of interventions: what is the impact of AEMPS
warnings and cost sharing change on osteoporosis drug
use?
 To assess changes in the utilization of osteoporosis drugs in the region of
Valencia (Spain) after safety warnings from regulatory agencies and cost‐sharing
changes, according to patient socio‐demographic and risk of fracture
characteristics.
 Monthly series of osteoporosis drug consumption for 2009‐2015 from the
ESOSVAL cohort (n=11,035; women: 48%; mean age: 65 years old)
 Interrupted time series and segmented linear regression models to assess
changes in osteoporosis drug utilization while controlling for previous levels and
trends after three natural intervention dates:
 the issue of the Spanish Agency for Drugs and Medical Products (AEMPS)
Osteonecrosis Jaw Warning (Sept 2009),
 the AEMPS Atypical femur Fracture Warning (Apr 2011),
 and the modification of the cost‐sharing scheme (Jul 2012).

25. Impact of interventions: what is the impact of AEMPS
warnings and cost sharing change on osteoporosis drug use
in high and low risk patients?

26. Impact of interventions: what is the impact of AEMPS
warnings and cost sharing change on osteoporosis drug
use?
 The AEMPS Osteonecrosis Jaw Warning was not associated with a
decline in the consumption of osteoporosis drugs.
 The warning on Atypical Fracture (a downward trend of 0.11% fewer
people treated each month) and the increase in the cost‐sharing
scheme (immediate change level of ‐1.07% in the proportion of people
treated) were associated with a strong decline in the proportion of
patients treated.
 By the end of 2015 osteoporosis drug consumption was around half
that of 2009. The relative decline was similar in people with both a high
and low risk of fracture.

27. Garcia_ani@gva.es
That’s all folks!