Mobilising Collaboration among Stakeholders to Optimise the Growing Potential of Data for Tackling Cancer
Abstract
:1. Introduction
2. Materials and Methods
2.1. Series of Expert Panels
2.2. A Survey of Patients
- -
- Cancer prevention;
- -
- Screening and early diagnosis;
- -
- Sensitivity and resistance to therapy;
- -
- Paediatric cancer;
- -
- Cancer and ageing;
- -
- Survivorship and quality of life;
- -
- Data sharing.
2.2.1. Data Analysis
Cancer Type
Statistical Analysis
Country-Wise and Region-Wise Analysis
2.3. A Survey of Healthcare Professionals
3. Results
3.1. Series of Expert Panels
3.2. A Survey of Patients
3.2.1. Cancer Types
ANOVA—Cancer Types
t-Test
3.2.2. Country-Wise
Correlation
ANOVA—Country-Wise
3.2.3. Region-Wise
Correlation
ANOVA—Region-Wise
3.3. A Survey to Healthcare Professionals
4. Discussion
4.1. Key Findings
4.1.1. Infrastructure
4.1.2. Prevention
4.1.3. Treatment
4.1.4. Genomics Data Sharing
4.1.5. Correlation among Studies
4.2. The Study’s Strengths and Limitations
4.2.1. Strengths of the Study
4.2.2. Limitations of the Study
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Member State | Governance Body | Exists at National Level | Public Sector Entity | Charges Access Fees | Hosts Data | Provides Access to Prescribing and Dispensing Records, and to Hospital Electronic Health Records |
---|---|---|---|---|---|---|
Bulgaria | The National Centre of Public Health and Analyses (NCPHA) | √ | √ | √ | √ | √ |
Cyprus | The Ministry of Health and the National Bioethics Committee | √ | √ | x | x | x |
Denmark | Statistics Denmark and the Danish Health Data Authority | √ | √ | √ | √ | √ |
Finland | Findata—an independent central agency | √ | √ | √ | √ | x |
France | The Health Data Hub—builds on previous initiatives | √ | √ | x | √ | √ |
Germany | The Research Data Centre at BfArM; National Research Data Infrastructure (NFDI); and Medical Informatics Initiative (MII) | √ | √ | √ | √ | √ |
Ireland | NREC COVID19 (National Research Ethics Committee for COVID-19) | √ | √ | x | x | x |
Latvia | The Centre for Disease Prevention and Control (SPKC) | √ | √ | x | x | x |
Netherlands | Statistics Netherland (CBS) | √ | √ | √ | √ | √ |
Portugal | SPMS—Shared Services of Ministry of Health | √ | √ | x | x | √ |
Standard | Description |
---|---|
OHDSI/OMOP | The concept—to transform data contained within those databases into a common format. |
CIMI | It is used for managing cloud infrastructure. This specification standardises interactions between cloud environments. |
mCODE | mCODE is a step towards capturing research-quality data from the treatment of all cancer patients. |
ICGC ARGO | It aims to accelerate research in genomic oncology, aligned by FAIR data principles. |
OSIRIS | The OSIRIS network has proposed a list of 130 clinical and -omic items and establishes a minimum dataset for the sharing of clinico-biological data in oncology. |
CDISC CDASH | CDASH establishes a standard method of collecting data consistently across studies and sponsors. |
OpenEHR | OpenEHR is a non-profit organization that publishes technical standards for an EHR platform along with domain-developed clinical models to define content. |
HL7 FHIR | FHIR consists of two main parts: a content model in the form of ‘resources’, and a specification for the exchange of these resources. |
Measure ID | Pillar ID | Measure Name |
---|---|---|
1 | 1 | Gut bacteria and diet |
2 | 1 | Metabolism and exercise |
3 | 1 | Chronic inflammation |
4 | 1 | Substances causing cancer in the environment |
5 | 1 | Prevention of cancer |
6 | 1 | Cancer heredity and epigenetics |
7 | 2 | Processes occurring before tumour development |
8 | 2 | Early cancer mechanisms |
9 | 2 | Blood tests for early detection |
10 | 2 | Technologies for early diagnosis |
11 | 2 | Personalised prevention and early screening |
12 | 3 | Blood tests to show sensitivity and resistance to therapy |
13 | 3 | The biology of cancer cells (immune system, stem cells, microenvironment, genetics, etc.) |
14 | 3 | New therapeutic approaches and drug delivery systems |
15 | 4 | Hereditary cancer and epigenetic mechanisms in paediatric cancer |
16 | 4 | Cancer and development |
17 | 4 | Therapeutic strategies in paediatric cancer |
18 | 4 | The study of the immune system relating to paediatric cancers |
19 | 4 | Pregnancy and paediatric cancer links |
20 | 5 | Determinants of ageing and cancer |
21 | 5 | The cell biology of ageing and cancer |
22 | 5 | Ageing and the process of ageing in cancer |
23 | 5 | Influence of ageing on cancer interventions |
24 | 5 | Cancer complications and comorbidities |
25 | 6 | Secondary cancers associated with treatment |
26 | 6 | Long-term immune-related side effects |
27 | 6 | Effects on reproductive functions and fertility |
28 | 6 | Effects on the fitness of the heart and lungs and the hormone system |
29 | 6 | Cancer treatments’ effects on the nervous system |
30 | 6 | Comprehensive management and care in cancer survivors |
31 | 7 | Generation of data |
32 | 7 | Use of data |
33 | 7 | Collection of data |
34 | 7 | Quality of data |
35 | 7 | Control of data sharing |
* Austria | * Belgium | * Bulgaria | * Croatia | * France | * Germany | * Ireland | * Italy |
0.555289021 | 0.555289021 | 0.555289021 | 0.6800873807 | 0.6800873807 | 0.6800873807 | 0.555289021 | 0.6800873807 |
* Netherlands | * Poland | * Portugal | * Romania | * Slovenia | * Spain | * Sweden | * UK |
0.555289021 | 0.6800873807 | 0.555289021 | 0.555289021 | 0.6800873807 | 0.555289021 | 0.6800873807 | 0.555289021 |
Measure ID | Pillar ID | Correlation among Age Group 16–44 and 45 to 70+ | Measure ID | Pillar ID | Correlation among Age Group 16–44 and 45 to 70+ | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Prostate Cancer | Breast Cancer | Other GI Cancer | Lung Cancer | Colon Cancer | Prostate Cancer | Breast Cancer | Other GI Cancer | Lung Cancer | Colon Cancer | ||||
1 | 1 | 0.9262729768 | 0.9763985608 | 0.8487446815 | 0.8646804113 | 0.6405452936 | 19 | 4 | 0.9293215856 | 0.9708945851 | 0.8900284863 | 0.9630879641 | 0.9093697261 |
2 | 1 | 0.9792369451 | 0.9518528105 | 0.4759633346 | 0.9699668008 | 0.8338111971 | 20 | 5 | 0.5895769129 | 0.9204739065 | 0.6392720318 | 0.7766158214 | 0.7804118135 |
3 | 1 | 0.9426662451 | 0.9622156264 | 0.8555635306 | 0.9198346707 | 0.940235275 | 21 | 5 | 0.8692067099 | 0.9672213565 | 0.6337694342 | 0.9050656066 | 0.9825607014 |
4 | 1 | 0.9551150096 | 0.9933345226 | 0.5760184329 | 0.9967667326 | 0.9522085247 | 22 | 5 | 0.7280205052 | 0.9660436937 | 0.6657540493 | 0.9381969473 | 0.9496349292 |
5 | 1 | 0.7173377939 | 0.9764060315 | 0.6492344541 | 0.996724258 | 0.9482153027 | 23 | 5 | 0.9356056042 | 0.9047858304 | 0.9802848037 | 0.8812820747 | 0.9526675178 |
6 | 1 | 0.8261856174 | 0.9825588025 | 0.8401680504 | 0.9789263674 | 0.9834989102 | 24 | 5 | 0.9181395839 | 0.8695867927 | 0.9633134713 | 0.9772372362 | 0.9848702577 |
7 | 2 | 0.7510020045 | 0.9788963057 | 0.9317497214 | 0.9671450826 | 0.9654771736 | 25 | 6 | 0.9231612202 | 0.9474972496 | 0.8486684248 | 0.9340932514 | 0.9272500256 |
8 | 2 | 0.9796940745 | 0.9958650548 | 0.9511127087 | 0.9645230348 | 0.9893688733 | 26 | 6 | 0.9578051962 | 0.978148731 | 0.9903266083 | 0.9745558381 | 0.9867799415 |
9 | 2 | 0.8892565366 | 0.9885884427 | 0.6691886215 | 0.982713894 | 0.9206295003 | 27 | 6 | 0.599169005 | 0.9939258201 | 0.8604007285 | 0.7434563018 | 0.8945464718 |
10 | 2 | 0.9961325275 | 0.9924888697 | 0.8475868968 | 0.9905950025 | 0.9824487063 | 28 | 6 | 0.9713573622 | 0.9400245527 | 0.8076071442 | 0.8600469129 | 0.9874276464 |
11 | 2 | 0.9578967429 | 0.994784039 | 0.7615333941 | 0.9842980462 | 0.9223883191 | 29 | 6 | 0.9398071268 | 0.9131692578 | 0.794661488 | 0.983620699 | 0.9867954289 |
12 | 3 | 0.9282676528 | 0.9940364689 | 0.8432993811 | 0.7415841485 | 0.985889568 | 30 | 6 | 0.7463822355 | 0.9734306348 | 0.8791278938 | 0.9260381343 | 0.9937876573 |
13 | 3 | 0.8256412638 | 0.9962001905 | 0.7287986972 | 0.9681373549 | 0.8816925665 | 31 | 7 | 0.8674952622 | 0.9815228558 | 0.7463517925 | 0.9298557579 | 0.9092466457 |
14 | 3 | 0.8459105317 | 0.9977314405 | 0.6085252566 | 0.9964173969 | 0.9738061765 | 32 | 7 | 0.9427408481 | 0.9928073176 | 0.7627127698 | 0.955390757 | 0.9635074976 |
15 | 4 | 0.9112157654 | 0.9568390514 | 0.8861469462 | 0.9899033754 | 0.9738545441 | 33 | 7 | 0.9346195356 | 0.9891003526 | 0.8330863022 | 0.9875066023 | 0.9859769052 |
16 | 4 | 0.9334275392 | 0.9670211012 | 0.8907986682 | 0.8890884822 | 0.9467274786 | 34 | 7 | 0.944349355 | 0.9981096555 | 0.7635590272 | 0.9703894361 | 0.975524558 |
17 | 4 | 0.9832343342 | 0.9683571491 | 0.7789808377 | 0.985301329 | 0.9079136585 | 35 | 7 | 0.8242193875 | 0.996571449 | 0.8553378061 | 0.9984137052 | 0.8889063933 |
18 | 4 | 0.9130407079 | 0.9601213137 | 0.7635590272 | 0.9902727838 | 0.9746393406 |
ANOVA Age 16–44 Years | ANOVA Age 45–70+ Years | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Groups | Count | Sum | Average | Variance | Groups | Count | Sum | Average | Variance | ||||
Prostate Cancer | 7 | 61.19533333 | 8.742190476 | 0.3029254392 | Prostate Cancer | 7 | 59.564 | 8.509142857 | 0.08881862434 | ||||
Breast Cancer | 7 | 60.42 | 8.631428571 | 0.1110049524 | Breast Cancer | 7 | 60.258 | 8.608285714 | 0.05729627513 | ||||
Other GI Cancer | 7 | 62.406 | 8.915142857 | 0.1186940317 | Other GI Cancer | 7 | 59.20866667 | 8.458380952 | 0.07387020106 | ||||
Lung Cancer | 7 | 61.02933333 | 8.71847619 | 0.1908039577 | Lung Cancer | 7 | 62.08666667 | 8.86952381 | 0.1601175873 | ||||
Colon Cancer | 7 | 62.07333333 | 8.867619048 | 0.09735168254 | Colon Cancer | 7 | 61.13866667 | 8.734095238 | 0.09707843386 | ||||
ANOVA | ANOVA | ||||||||||||
Source of Variation | SS | df | MS | F | p-value | F crit | Source of Variation | SS | df | MS | F | p-value | F crit |
Between Groups | 0.3717172571 | 4 | 0.09292931429 | 0.5661036276 | 0.6891235703 | 2.689627574 | Between Groups | 0.7879572571 | 4 | 0.1969893143 | 2.064093751 | 0.1104958938 | 2.689627574 |
Within Groups | 4.924680381 | 30 | 0.1641560127 | Within Groups | 2.86308673 | 30 | 0.09543622434 | ||||||
Total | 5.296397638 | 34 | Total | 3.651043987 | 34 |
Prostate Cancer (16–44 Years) | Prostate Cancer (45–70+ Years) | Breast Cancer (16–44 Years) | Breast Cancer (45–70+ Years) | Other GI Cancer (16–44 Years) | Other GI Cancer (45–70+ Years) | |||
---|---|---|---|---|---|---|---|---|
Mean | 8.746857143 | 8.488571429 | Mean | 8.607428571 | 8.584 | Mean | 8.88 | 8.427428571 |
Variance | 0.402257479 | 0.1218890756 | Variance | 0.1290843697 | 0.08253647059 | Variance | 0.2417411765 | 0.155554958 |
Observations | 35 | 35 | Observations | 35 | 35 | Observations | 35 | 35 |
Pearson Correlation | 0.7013193955 | Pearson Correlation | 0.8473585623 | Pearson Correlation | 0.4554390297 | |||
Hypothesised Mean Difference | 0 | Hypothesised Mean Difference | 0 | Hypothesised Mean Difference | 0 | |||
Df | 34 | df | 34 | df | 34 | |||
t Stat | 3.306532101 | t Stat | 0.7235750693 | t Stat | 5.699785618 | |||
P(T <= t) one-tail | 0.001118223344 | P(T <= t) one-tail | 0.237137899 | P(T <= t) one-tail | 0.000001057268417 | |||
t Critical one-tail | 1.690924255 | t Critical one-tail | 1.690924255 | t Critical one-tail | 1.690924255 | |||
P(T <= t) two-tail | 0.002236446687 | P(T <= t) two-tail | 0.4742757979 | P(T <= t) two-tail | 0.000002114536834 | |||
t Critical two-tail | 2.032244509 | t Critical two-tail | 2.032244509 | t Critical two-tail | 2.032244509 | |||
Lung Cancer (16–44 Years) | Lung Cancer (45–70+ Years) | Colon Cancer (16–44 Years) | Colon Cancer (45–70+ Years) | |||||
Mean | 8.664 | 8.819428571 | Mean | 8.853142857 | 8.724571429 | |||
Variance | 0.2763952941 | 0.2562937815 | Variance | 0.1428457143 | 0.1325431933 | |||
Observations | 35 | 35 | Observations | 35 | 35 | |||
Pearson Correlation | 0.8861803253 | Pearson Correlation | 0.6430274191 | |||||
Hypothesised Mean Difference | 0 | Hypothesised Mean Difference | 0 | |||||
Df | 34 | df | 34 | |||||
t Stat | −3.72407529 | t Stat | 2.424455364 | |||||
P(T <= t) one-tail | 0.0003542980939 | P(T <= t) one-tail | 0.01039868626 | |||||
t Critical one-tail | 1.690924255 | t Critical one-tail | 1.690924255 | |||||
P(T <= t) two-tail | 0.0007085961878 | P(T <= t) two-tail | 0.02079737252 | |||||
t Critical two-tail | 2.032244509 | t Critical two-tail | 2.032244509 |
Correlation of Measures | Belgium | Bulgaria | France | Germany | Greece | Hungary | Italy | Luxembourg | Netherlands | Portugal | Romania | Slovakia | Spain |
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Belgium | 1 | 0.6475557544 | 0.7498715593 | 0.7373399003 | 0.5966926059 | 0.6820707105 | 0.8758841021 | 0.8158282088 | 0.7832400965 | 0.7592304592 | 0.6411849041 | 0.7807426551 | 0.7740452942 |
Bulgaria | 0.6475557544 | 1 | 0.5872358953 | 0.526827931 | 0.6416441335 | 0.6522617738 | 0.7275400968 | 0.651953271 | 0.5424191801 | 0.7563903232 | 0.5581737268 | 0.497672728 | 0.5682001369 |
France | 0.7498715593 | 0.5872358953 | 1 | 0.8760069617 | 0.6696757517 | 0.6204217458 | 0.7552368839 | 0.8668425773 | 0.8424594551 | 0.8202164339 | 0.3636101418 | 0.726664892 | 0.8093951744 |
Germany | 0.7373399003 | 0.526827931 | 0.8760069617 | 1 | 0.5909595653 | 0.6856612166 | 0.7646475056 | 0.8462114174 | 0.8188253529 | 0.8183372714 | 0.4183946737 | 0.7588983554 | 0.8298239223 |
Greece | 0.5966926059 | 0.6416441335 | 0.6696757517 | 0.5909595653 | 1 | 0.69191804 | 0.7047573668 | 0.6330712976 | 0.5210158628 | 0.7177081717 | 0.3419602124 | 0.5174391845 | 0.5274675803 |
Hungary | 0.6820707105 | 0.6522617738 | 0.6204217458 | 0.6856612166 | 0.69191804 | 1 | 0.8178145204 | 0.5697028129 | 0.6202496596 | 0.7811291529 | 0.6693190159 | 0.6379146628 | 0.7479369975 |
Italy | 0.8758841021 | 0.7275400968 | 0.7552368839 | 0.7646475056 | 0.7047573668 | 0.8178145204 | 1 | 0.8158654563 | 0.7377974028 | 0.8558434446 | 0.6707957314 | 0.7731998664 | 0.8771925745 |
Luxembourg | 0.8158282088 | 0.651953271 | 0.8668425773 | 0.8462114174 | 0.6330712976 | 0.5697028129 | 0.8158654563 | 1 | 0.7896559606 | 0.7647657601 | 0.460745273 | 0.7854106064 | 0.8253155835 |
Netherlands | 0.7832400965 | 0.5424191801 | 0.8424594551 | 0.8188253529 | 0.5210158628 | 0.6202496596 | 0.7377974028 | 0.7896559606 | 1 | 0.7584418 | 0.4832417907 | 0.7347269057 | 0.7454833323 |
Portugal | 0.7592304592 | 0.7563903232 | 0.8202164339 | 0.8183372714 | 0.7177081717 | 0.7811291529 | 0.8558434446 | 0.7647657601 | 0.7584418 | 1 | 0.5145866079 | 0.6896646652 | 0.8150854327 |
Romania | 0.6411849041 | 0.5581737268 | 0.3636101418 | 0.4183946737 | 0.3419602124 | 0.6693190159 | 0.6707957314 | 0.460745273 | 0.4832417907 | 0.5145866079 | 1 | 0.5951574127 | 0.611582652 |
Slovakia | 0.7807426551 | 0.497672728 | 0.726664892 | 0.7588983554 | 0.5174391845 | 0.6379146628 | 0.7731998664 | 0.7854106064 | 0.7347269057 | 0.6896646652 | 0.5951574127 | 1 | 0.7522836171 |
Spain | 0.7740452942 | 0.5682001369 | 0.8093951744 | 0.8298239223 | 0.5274675803 | 0.7479369975 | 0.8771925745 | 0.8253155835 | 0.7454833323 | 0.8150854327 | 0.611582652 | 0.7522836171 | 1 |
Groups | Count | Sum | Average | Variance | ||
---|---|---|---|---|---|---|
Belgium | 35 | 147.78 | 4.222285714 | 0.06212991597 | ||
Bulgaria | 35 | 149.16 | 4.261714286 | 0.04168521008 | ||
France | 35 | 150.01 | 4.286 | 0.04938941176 | ||
Germany | 35 | 154.26 | 4.407428571 | 0.04990201681 | ||
Greece | 35 | 151.9 | 4.34 | 0.03373529412 | ||
Hungary | 35 | 149.88 | 4.282285714 | 0.01812991597 | ||
Italy | 35 | 151.66 | 4.333142857 | 0.01771042017 | ||
Luxembourg | 35 | 151.68 | 4.333714286 | 0.06163579832 | ||
Netherlands | 35 | 144.43 | 4.126571429 | 0.05454672269 | ||
Portugal | 35 | 159.38 | 4.553714286 | 0.02323579832 | ||
Romania | 35 | 152.54 | 4.358285714 | 0.01585579832 | ||
Slovakia | 35 | 150.79 | 4.308285714 | 0.03932638655 | ||
Spain | 35 | 155.53 | 4.443714286 | 0.0318005042 | ||
ANOVA | ||||||
Source of Variation | SS | df | MS | F | p-value | F crit |
Between Groups | 4.625164835 | 12 | 0.3854304029 | 10.03959922 | 0 | 1.774100948 |
Within Groups | 16.96882857 | 442 | 0.03839101487 | |||
Total | 21.59399341 | 454 |
Measure Correlations | Western | Southern | Northern | Eastern |
---|---|---|---|---|
Western | 1 | |||
Southern | 0.9018220214 | 1 | ||
Northern | 0.7201564076 | 0.7969578047 | 1 | |
Eastern | 0.818921507 | 0.9250914256 | 0.8238586252 | 1 |
Groups | Count | Sum | Average | Variance | ||
---|---|---|---|---|---|---|
Western | 35 | 149.8 | 4.28 | 0.04678235294 | ||
Southern | 35 | 153.21 | 4.377428571 | 0.02011966387 | ||
Northern | 35 | 149.7 | 4.277142857 | 0.09166218487 | ||
Eastern | 35 | 150.46 | 4.298857143 | 0.01865747899 | ||
ANOVA | ||||||
Source of Variation | SS | df | MS | F | p-value | F crit |
Between Groups | 0.232385 | 3 | 0.07746166667 | 1.74835644 | 0.1600884797 | 2.671177951 |
Within Groups | 6.025537143 | 136 | 0.04430542017 | |||
Total | 6.257922143 | 139 |
Question | Basic Clinical Lab/Research Centre (n = 9) | Clinical Cancer Centre (n = 19) | Comprehensive Cancer Centre (n = 35) | Total |
---|---|---|---|---|
Is your institution sharing genomic data with other institutions in the same country or cross-border? | ||||
No, it is not sharing | 88.89% | 21.05% | 40.00% | 41.27% |
Yes, at national level | 0.00% | 47.37% | 22.86% | 26.98% |
Yes, both at the national and cross-border level | 11.11% | 26.32% | 34.29% | 28.57% |
Yes, cross-border | 0.00% | 5.26% | 0.00% | 1.59% |
Not available | 0.00% | 0.00% | 2.86% | 1.59% |
Main purpose of genomic data in your center? | ||||
For research | 22.22% | 0.00% | 8.57% | 7.94% |
For clinical trials | 11.11% | 5.26% | 5.71% | 6.35% |
For research and clinical trials | 22.22% | 78.95% | 80.00% | 71.43% |
Nothing listed | 44.44% | 15.79% | 2.86% | 12.70% |
Not available | 0.00% | 0.00% | 2.86% | 1.59% |
Which best describes what information you provide to patients/citizens after involving them in NGS testing? | ||||
No information is provided | 11.11% | 0.00% | 2.86% | 3.17% |
Report on any positive biomarkers and relevant treatments | 44.44% | 47.37% | 42.86% | 44.44% |
A summarized NGS testing report | 22.22% | 26.32% | 42.86% | 34.92% |
Full NGS testing report | 22.22% | 26.32% | 8.57% | 15.87% |
Not available | 0.00% | 0.00% | 2.86% | 1.59% |
What type of information do you provide to patients/citizens before involving them in NGS testing? | ||||
No information is provided | 11.11% | 0.00% | 8.57% | 6.35% |
Limitations of the test | 0.00% | 5.26% | 2.86% | 3.17% |
Type of analysis | 11.11% | 0.00% | 11.43% | 7.94% |
Risks and benefits of the test | 11.11% | 10.53% | 25.71% | 19.05% |
Everything listed here (limitations, type of analysis, risks and benefits of the test) | 66.67% | 84.21% | 51.43% | 63.49% |
Do you link data from sequenced genomes to clinical data (Electronic HealthRecords) or other types of data (e.g., biobanks, proteomics...)? | ||||
No, there is no linking | 33.33% | 21.05% | 22.86% | 23.81% |
Yes, it is done on request | 33.33% | 15.79% | 28.57% | 25.40% |
Yes, it is done regularly | 33.33% | 63.16% | 48.57% | 50.79% |
Funding and Allocation of Resources | ||||
For NGS testing used for clinical care for appropriate patients, how are the majority of tests funded for the majority of citizens that receive an NGS result? | ||||
Institution-based research grant/funding | 11.11% | 5.26% | 8.57% | 7.94% |
National or regional healthcare system | 66.67% | 84.21% | 80.00% | 79.37% |
Private or public—Supplementary insurance | 11.11% | 5.26% | 11.43% | 9.52% |
Industry funded | 11.11% | 5.26% | 0.00% | 3.17% |
Are you familiar with different business models on data? If yes, which one of listed is most sustainable in your opinion? | ||||
All of these, for our type of activity | 0.00% | 0.00% | 2.86% | 1.59% |
Open access (FAIR = FREE) | 11.11% | 0.00% | 14.29% | 9.52% |
Channel priced models on data | 11.11% | 0.00% | 11.43% | 7.94% |
Capacity rationed access | 0.00% | 5.26% | 2.86% | 3.17% |
Proprietary business model on data | 11.11% | 10.53% | 11.43% | 11.11% |
None of these | 11.11% | 5.26% | 11.43% | 9.52% |
I am not familiar | 55.56% | 78.95% | 45.71% | 57.14% |
Member State | Data Infrastructure | Registry |
---|---|---|
Croatia | -Pseudoanonymised cancer data—available in the National Cancer Registry -Data integration—works well at the primary care level -Little standardisation of data at the secondary care level | Croatian National Cancer Registry |
Hungary | -Data links between the screening registry and national registries facilitate the functioning of national early detection programs -The obstacle—a lack of financial and human resources | Hungarian Central Statistical Office National Institute of Health Insurance Fund Management National Cancer Registry |
Netherlands | -Data on diagnostics, follow-up care and survival–collected in cancer registry -Nationwide Pathology Databank provides data on pathological diagnosis | Netherlands Cancer Registry (NCR) |
France | -French Health Data Hub—AI technologies could provide a strategic advantage for the nation | The French National Registry of Childhood Cancers (RNCE) |
Portugal | -Patient-reported data are not yet embedded in information systems | National Cancer Registry |
Poland | -E-health services–have a significant role, more than 60% of the population used remote health services in the first year of the pandemic -The COVID-19 pandemic had a positive impact on the process of ongoing data collection in cancer registries and timely analysis | National Cancer Registry |
Italy | -Regional cancer registries collect and transfer data to the national registry | A comprehensive national cancer registry—currently in development |
Denmark | -After screening is completed, data are stored in the central registry, comprising a comprehensive set of healthcare data | Highly digitalised Central Person Registry |
Belgium | -Systematic data collection for all cancer cases | Belgian Cancer Registry |
Germany | -Increasing interoperability and use of cancer datasets is a priority | Cancer Registry Data |
Finland | -Registry—links sociodemographic data with medical records via unique patient identifiers | Cancer Registry |
Spain | -Registries—record all new cancer cases diagnosed in a specific location | Population-based cancer registries |
Ireland | -Cancer data collection—becoming increasingly electronic | National Cancer Registry |
Slovenia | -Surveillance data along the whole continuum of cancer care are provided | Slovenian Cancer Registry |
Bulgaria | -Data infrastructure to monitor the burden of cancer and outcomes of care—not fully operational | National Cancer Registry |
Romania | -Lack of systematic collection of data in a national cancer registry | National cancer registry |
Austria | -The main data source for the epidemiology, diagnosis and treatment of cancer is the National Cancer Registry | National Cancer Registry |
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Share and Cite
Horgan, D.; Van den Bulcke, M.; Malapelle, U.; Normanno, N.; Capoluongo, E.D.; Prelaj, A.; Rizzari, C.; Stathopoulou, A.; Singh, J.; Kozaric, M.; et al. Mobilising Collaboration among Stakeholders to Optimise the Growing Potential of Data for Tackling Cancer. J. Mol. Pathol. 2023, 4, 234-258. https://doi.org/10.3390/jmp4040021
Horgan D, Van den Bulcke M, Malapelle U, Normanno N, Capoluongo ED, Prelaj A, Rizzari C, Stathopoulou A, Singh J, Kozaric M, et al. Mobilising Collaboration among Stakeholders to Optimise the Growing Potential of Data for Tackling Cancer. Journal of Molecular Pathology. 2023; 4(4):234-258. https://doi.org/10.3390/jmp4040021
Chicago/Turabian StyleHorgan, Denis, Marc Van den Bulcke, Umberto Malapelle, Nicola Normanno, Ettore D. Capoluongo, Arsela Prelaj, Carmelo Rizzari, Aliki Stathopoulou, Jaya Singh, Marta Kozaric, and et al. 2023. "Mobilising Collaboration among Stakeholders to Optimise the Growing Potential of Data for Tackling Cancer" Journal of Molecular Pathology 4, no. 4: 234-258. https://doi.org/10.3390/jmp4040021
APA StyleHorgan, D., Van den Bulcke, M., Malapelle, U., Normanno, N., Capoluongo, E. D., Prelaj, A., Rizzari, C., Stathopoulou, A., Singh, J., Kozaric, M., Dube, F., Ottaviano, M., Boccia, S., Pravettoni, G., Cattaneo, I., Malats, N., Buettner, R., Lekadir, K., de Lorenzo, F., ... Solary, E. (2023). Mobilising Collaboration among Stakeholders to Optimise the Growing Potential of Data for Tackling Cancer. Journal of Molecular Pathology, 4(4), 234-258. https://doi.org/10.3390/jmp4040021