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RESEARCH PROTOCOL:

Time trends in prevalence, incidence and survival

of cancer in the OHDSI Network

Version: 3.0

Date: 19th November 2024

Acknowledgement: The analysis is based in part on work from the Observational Health

Sciences and Informatics collaborative. OHDSI (http://ohdsi.org) is a multi-stakeholder,

interdisciplinary collaborative to create open-source solutions that bring out the value of

observational health data through large-scale analytics.

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Table of contents

1 List of Abbreviations ....................................................................................................... 3

2 Responsible Parties ......................................................................................................... 3

2.1 Investigators .............................................................................................................. 3

3 Abstract ............................................................................................................................ 4

5 Milestones ........................................................................................................................ 5

6 Rationale and Background .............................................................................................. 6

7 Study Objectives .............................................................................................................. 6

8 Research Methods ........................................................................................................... 7

8.1 Study Design and Study Period................................................................................ 7

8.2 Data Sources ............................................................................................................. 7

8.3 Study Population ....................................................................................................... 7

8.4 Cancer prevalence, incidence and survival ............................................................. 8

8.5 Stratifications............................................................................................................. 8

8.6 Other variables .......................................................................................................... 8

8.7 Data quality assessment ........................................................................................... 9

8.8 Analysis...................................................................................................................... 9

9 Sample Size and Study Power ...................................................................................... 11

10 Strengths and Limitations ........................................................................................... 11

10.1 Strengths ................................................................................................................ 11

10.2 Limitations ............................................................................................................. 11

11 Protection of Human Subjects .................................................................................... 11

13 Plans for Disseminating and Communicating Study Results ................................... 12

References ........................................................................................................................ 12

Appendix 1. Preliminary code list of cancer ................................................................... 14

Appendix 2. Preliminary code list of conditions ............................................................. 14

Appendix 3. Preliminary code list of medications for large-scale characterizaton ...... 18

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1 List of Abbreviations

AAPC Average Annual Percent Change

APC Annual Percent Change

CDM Common Data Model

COPD Chronic Obstructive Pulmonary Disease

EHDEN European Health Data and Evidence Network

HIV Human Immunodeficiency Virus

HPV Human Papillomavirus Infection

IRB Institutional Review Board

IR Incidence Rate

IRR Incidence Rate Ratio

OMOP Observational Medical Outcomes Partnership

OHDSI Observational Health Data Science and Informatics

PR Prevalence Rate

SIDIAP Information System for Reseach in Primary Care

SNOMED Systematized Nomenclature of Medicine

WHO World Health Organization

2 Responsible Parties

2.1 Investigators

Investigator/Author Institution/Affiliation

Talita Duarte-Salles* Fundació Institut Universitari per a la recerca a l'Atenció

Primària de Salut Jordi Gol i Gurina (IDIAPJGol),

Barcelona, Spain

Department of Medical Informatics, Erasmus University

Medical Center, Rotterdam, The Netherlands

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Edward Burn University of Oxford

Asieh Golozar Odysseus Data Services, Inc, Cambridge, MA USA,

OHDSI Center at the Northeastern University, Boston,

MA USA

Irene López Sánchez Fundació Institut Universitari per a la recerca a l'Atenció

Primària de Salut Jordi Gol i Gurina (IDIAPJGol),

Barcelona, Spain

Laura Pérez Crespo Fundació Institut Universitari per a la recerca a l'Atenció

Primària de Salut Jordi Gol i Gurina (IDIAPJGol),

Barcelona, Spain

Agustina Giuliodori Picco Fundació Institut Universitari per a la recerca a l'Atenció

Primària de Salut Jordi Gol i Gurina (IDIAPJGol),

Barcelona, Spain

Berta Raventós Department of Medical Informatics, Erasmus University

Medical Center, Rotterdam, The Netherlands

Anna Palomar Cros Fundació Institut Universitari per a la recerca a l'Atenció

Primària de Salut Jordi Gol i Gurina (IDIAPJGol),

Barcelona, Spain

Anton Barchuk Department of Medical Informatics, Erasmus University

Medical Center, Rotterdam, The Netherlands

OHDSI Oncology WG

*Principal Investigator

Authorship in scientific manuscripts will follow ICMJE authorship criteria

(http://www.icmje.org/recommendations/browse/roles-and-responsibilities/defining-the-role-

of-authors-and-contributors.html). The Responsible Parties involved in this protocol take

accountability for the overarching protocol, package development, providing assistance to

sites running the analysis and ensuring site-specific governance is adhered to in all

publications generated from this protocol.

3 Abstract

Objectives: The main aim of this project is to estimate time trends in prevalence and

incidence rates, and short- and long-term survival of site-specific cancers in the OHDSI

network.

Design: This study will be a multinational observational cohort study and will be conducted

using a network of large real world data sources that have been mapped to the

Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).

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Setting: Population-based, electronic health records, claims and registry data from primary

and secondary care.

Participants: Individuals with no prior history of cancer (for incidence and survival analyses

only), and who have been on the database for at least 1 year before study entry.

Outcomes: Prevalent and incident cancer diagnoses and overall as well as 1-, 5-, and 10-

year survival of site-specific cancers.

Data analyses: The OHDSI Cohort Diagnostics package will be used to assess the fitness

of use of cancer data on each database. We will calculate prevalence (PR) and incidence

rates (IR) with 95% confidence intervals (95%CI) for each year and study period by dividing

the number of ever and first recorded cases of cancer, respectively, by 1,000 person-years

of follow-up, overall and stratified by demographics and relevant comorbidities. The overall

and 1-, 5-, and -10-year survival rates will be calculated as the percentage of people who

have been diagnosed with cancer and are still alive during the study period as well as one or

five years after diagnosis, respectively, per year and stratified by pre-defined subgroups. To

assess the incidence trend over time, we will calculate the IRs in 5 year periods and then

calculate the incidence rate ratios (IRRs) and their corresponding 95%CI to analyze the

differences in incidence between the defined time periods.

4 Amendments and Updates

Number Date Section of

study

protocol

Amendment or

update

Reason

Version 2.0 13/06/2023 All Update Update to incorporate

team comments and

suggestions

Version 3.0 19/11/2024 All Amendment Amendment to

incorporate a new

objective to the study

5 Milestones

Milestone Planned date

Final version of study protocol 20/11/2024

Create phenotype definitions 30/11/2024

Study package release 30/01/2025

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Data analyses 01/03/2025

Writing of scientific paper(s) 01/06/2025

6 Rationale and Background

Cancer is currently a leading cause of morbidity and mortality worldwide. Estimates from the

World Health Organization (WHO) in 2019 showed that cancer is the first or second leading

cause of death before the age of 70 years in 112 of 183 countries and ranks third or fourth in

a further 23 countries.1 The burden of cancer incidence and mortality is rapidly growing

worldwide, reflecting both aging and growth of the population as well as changes in the

prevalence and distribution of the main risk factors for cancer.2,3 Worldwide, female breast

cancer is the most commonly diagnosed cancer, followed by lung, colorectal, prostate, and

stomach cancers, while lung cancer is the leading cause of cancer death, followed by

colorectal, liver, stomach, and female breast cancers.4 The continuous surveillance and

monitoring of trends in cancer incidence and survival are needed for the development,

implementation and evaluation of health policies aiming to reduce the burden of disease.

Multiple factors are attributed to cancer incidence and cancer survival. Some of the main

cancer risk factors include age, race, smoking, alcohol consumption, cardiometabolic

conditions (e.g.: cardiovascular diseases, obesity, hypertension, type 2 diabetes mellitus),

infectious diseases (e.g.: hepatitis B/C, human immunodeficiency virus (HIV)), among

others. 5,6 Survival rates are known to vary by cancer type as well as being influenced by

cancer stage at detection and treatment. It can also depend on an individual’s health,

presence of comorbidities and other tumour-related factors. Recent advances in screening

programmes and treatments have improved the survival rates for some cancer sites. 7

However, important disparities still exist in healthcare systems and countries. 7,8

Understanding the complexities of individual factors related to cancer incidence and survival,

whilst investigating the population subgroups that are more prone to be at risk, is important

for being able to plan future population-based interventions. 9

Real world data from healthcare services promise to vastly expand clinical research,

providing data for large-scale studies that would not be feasible with traditional research data

collection methods. The Observational Health Data Sciences and Informatics (OHDSI;

www.ohdsi.org) is a multi-stakeholder and interdisciplinary international network that

generates open science through large-scale analytics of real world data. 10 This data network

offers a unique opportunity to study cancer incidence and survival as it includes more than

100 healthcare databases from 20 countries including over 1.5 billion individual records with

longitudinal data. OHDSI analyses are based around the use of the Observational Medical

Outcomes Partnership (OMOP) Common Data Model (CDM), which is used to standardize

otherwise disparate datasets. In this project, we will estimate time trends in prevalence,

incidence rates and survival of cancers in the OHDSI network.

7 Study Objectives

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The main aim of this project is to estimate time trends in prevalence, incidence rates and

short- and long-term survival of site-specific cancers in the OHDSI network.

The specific study objectives include:

1) To estimate prevalence rates of site-specific cancers by calendar year, age, sex, and

comorbidities.

2) To estimate incidence rates of site-specific cancers by calendar year, age, sex, and

comorbidities.

3) To describe demographic, clinical characteristics and medications of individuals with

cancer at the time of diagnosis.

4) To estimate overall, short- and long-term survival of site-specific cancers by calendar

year, age, sex, and comorbidities.

8 Research Methods

8.1 Study Design and Study Period

This study will be a multinational observational cohort study. The study period will start on

1st January 2000 or one year after the earliest date of available data in each database and

span up to the 31st December 2024 (or the last date of available data in each database).

8.2 Data Sources

The study will be conducted using a network of large real world data sources that have been

mapped to the OMOP Common Data Model in collaboration with the OHDSI and European

Health Data and Evidence Network (EHDEN) initiatives. The OMOP Common Data Model

(https://github.com/OHDSI/CommonDataModel/wiki) includes a standard representation of

health care experiences (such as information related to drug utilization and condition

occurrence), as well as common vocabularies for coding clinical concepts, and enables

consistent application of analyses across multiple disparate data sources.10 The present

study will be conducted in multiple databases in the OHDSI network willing to participate.

Databases that have already committed to participate include: the Information System for

Research in Primary Care (SIDIAP; Spain), the Integrated Primary Care Information (IPCI,

The Netherlands), The Netherlands Cancer Registry (IKNL, The Netherlands), the Clinical

Practice Research Datalink (CPRD, UK).

8.3 Study Population

All individuals registered in each of the data sources will be eligible for inclusion in the study.

However, study participants will be required to have a year of prior history observed in the

database before contributing observation time. Participants with prior history of a diagnosis

of cancer (any, excluding non-melanoma skin cancer) any time prior to their index date will

be excluded (for incidence and survival estimation).

For incidence rates, study participants will begin contributing person time on the respective

date of the latest of the following: 1) study start date (1st January 2000 or the earliest date of

available data in in each of the data sources), 2) date at which they have sufficient prior

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history (defined as 365 days), 3) date at which they reach a minimum age (where age strata

are being considered).

Participants will stop contributing person time at the earliest date of the following: 1) study

end date (end of available data in each of the data sources), 2) date at which their

observation period ends, 3) the last day in which they have the maximum age (where age

strata are being considered). Where there are multiple age strata, study participants will

contribute to each strata while they satisfy the conditions of that strata (i.e. when they reach

the limit of one age strata they will begin contributing to the next).

8.4 Cancer prevalence, incidence and survival

Cancer prevalence and incidence will be defined as the occurrence for ever or the first time

in the person’s history, respectively. Conditions in the OMOP CDM use the Systematized

Nomenclature of Medicine (SNOMED) as the standard vocabulary for diagnosis codes. The

complete list of codes used to define each cancer site are available in Appendix 1.

For those databases with available data on date and/or cause of death, overall, 1-, 5-, and

10-year survival will be estimated. We will also report overall mortality, cancer-specific

mortality, and other-cause mortality when possible.

8.5 Stratifications

Each target cohort will be analysed in full and stratified on sex (male, female), age (0-9; 10-

19; 20-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80-89; 90-99; 100 and over), smoking status,

asthma, type 2 diabetes mellitus, hypertension, cardiovascular diseases, chronic obstructive

pulmonary disease (COPD), obesity, human immunodeficiency virus (HIV), human

papillomavirus infection (HPV), viral hepatitis, chronic kidney disease, autoimmune

conditions, and depression. These stratifications will only be performed in those databases

with available information on the variables listed above. The definition of these conditions

can be found in Appendix 2. Female breast cancer will be stratified by menopausal status

which will be defined based on age as pre- (less than 50 years) and post-menopausal (50

years or above). All stratum are pending meeting minimum reportable cell counts (as

specified by data owners).

8.6 Other variables

Large-scale patient-level characterization will be conducted at the time of diagnosis. Medical

history and medications use history will be assessed at any time prior cancer diagnosis and

up to 366 days before index date, for 365 days to 31 days before index date, for 30 to 1 day

before index date and at index date. We will also report medication use for 1 to 90 days post

index-date.

A list of pre-specified comorbidities and medications will be described. These will include:

• Medical History: Anemia, Anxiety, Asthma, Arterial fibrillation, Cerebrovascular disease,

Chronic kidney disease, Chronic liver disease, Chronic obstructive pulmonary disease

(COPD), Coronary arteriosclerosis, Chron’s Disease, Dementia, Depressive disorder,

Gastro-oesophageal reflux disease (GERD), GI-Bleeding, Heart failure, Human

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Immunodeficiency Virus (HIV), Human papillomavirus infection (HPV), Hyperlipidaemia,

Hypertension, Hypothyroidism, Ischemic Heart Disease, Myocardial Infraction, Non-

alcoholic Fatty-acid disease (NAFLD), Obesity, Osteoarthritis, Osteoporosis,

Pancreatitis, Peripheral vascular disease, Pneumonia, Psoriasis, Pulmonary embolism,

Renal impairment, Stroke, Type 1 Diabetes, Type 2 Diabetes, Ulcerative colitis, UTI,

Venous thromboembolism, Viral Hepatitis

• Medications: Agents acting on the renin-angiotensin system, Antibacterials for systemic

use, Antidepressants, Antiemetics, Antiepileptics, Anti-inflammatory and anti-rheumatic

drugs, Antineoplastics, Anti-psoriatic, Antithrombotic agents, Antivirals HCV, antivirals

HIV, Beta-blocking agents, Calcium channel blockers, Contraceptives, Diuretics, Drugs

for acid related disorders, Drugs for obstructive airway diseases, Drugs used in diabetes,

Immunosuppressants, Iron preparations, Lipid-modifying agents, Opioids, Psycholeptics

and Psychostimulants.

8.7 Data quality assessment

Overall data quality of the OHDSI Network databases are assessed by each data partner

using the data quality dashboard after data mapping. 10 We will assess the created

phenotypes including cancer events using a comprehensive cohort characterization tool, the

OHDSI’s CohortDiagnostic package (https://ohdsi.github.io/CohortDiagnostics/). For any

cohort and data source mapped to OMOP CDM, this package systematically generates

incidence rates (stratified by age, gender, calendar year, and database), cohort

characteristics (all comorbidities, drug use, procedures, health utilization) and the actual

codes found in the data triggering the various rules in the cohort definitions. The

CohortDiagnostics package works in two steps: 1) Generate the utilization results and

diagnostics against a data source and 2) Explore the generated utilization and diagnostics in

a user-friendly graphical interface R-Shiny app. These diagnostics provide a consistent

methodology to evaluate cohort definitions/phenotype algorithms across a variety of

observational databases, allowing to compare the overlap between alternative definitions.

This can allow researchers and stakeholders to understand the heterogeneity of source

coding for exposures and health outcomes as well as the impact of various inclusion criteria

on overall cohort counts. Data quality of cancer diagnoses will be assessed by comparing

the calculated incidence rates of events in a specific database with the incidence reported in

national/regional cancer registries.

8.8 Analysis

All analyses will be performed using code developed for the OHDSI Methods library and

executed by data owners using R and stratified by database. Each data partner will execute

the study code against their database containing patient-level data and will then return the

results set which will only contain aggregated data. The results from each of the contributing

data sites will then be combined in tables and figures. The code for this study will be made

publicly available at https://github.com/ohdsi-studies/.

To answer the study objectives, a diagnostic package, built off the OHDSI Cohort

Diagnostics (https://ohdsi.github.io/CohortDiagnostics/) library, will be included in the base

package as a preliminary step to assess the fitness of use of phenotypes on each database.

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If a database passes cohort diagnostics (as described in section 8.6), the full study package

will be executed.

To address objectives 1 and 2, we will first summarize the socio-demographics

characteristics and overall follow-up of individuals included in the study, with counts and

percentages for categorical variables and median and interquartile ranges (IQR) for

continuous variables. Incidence rates (IR) with 95% CI will be calculated using the R

package IncidencePrevalence (https://cran.r-

project.org/web/packages/IncidencePrevalence/index.html). IR will be computed for each year

and study period by dividing first-recorded diagnoses by person-years at risk for the whole

population or pre-defined subgroups (section 8.5). The first recorded for each outcome

category will be included as an incident episode. Each outcome will be assessed separately

and therefore, individuals will be able to contribute to one the incidence counts for more than

one outcome. To exclude prevalent cases from incidence calculations, individuals diagnosed

with any cancer will not be considered as eligible incident cases in future cohorts after the

date of their first diagnosis. To assess the incidence trend over time, we will calculate the

IRs in 5 year periods and then calculated the incidence rate ratios (IRRs) and their

corresponding 95% confidence intervals (95% CIs) to analyze the differences in incidence

between the defined time periods (to be defined depending on data availability).

We will conduct a large-scale patient-level characterization to address objective 3. For this

part of the analysis, we will use the package CohortCharacteristics (https://cran.r-

project.org/web/packages/CohortCharacteristics/index.html). Essentially, we will use the

function summariseLargeScaleCharacteristics, which is used to summarize the large-scale

characteristics of a cohort table. Medical history and medications will be assessed for any

time prior cancer diagnosis and up to 366 days before index date, for 365 days to 31 days

before index date, for 30 to 1 day before index date and at index date (see appendix 2 and

3). Medications will also be assessed 1 to 90 days post-index date. We will also compare

these characteristics in our cohorts of interest to the proportion of these diseases in a

random subsample of SIDIAP.

To address objective 4, the 1-, 5-, and 10-year survival rates will be calculated as the

percentage of people who have been diagnosed with cancer and are still alive one, five, or

ten years after diagnosis, respectively, per year, overall and also stratified by pre-defined

subgroups.

Survival will be calculated using data on time at risk of death from any cause and the

Kaplan-Meier method. Results will be reported as plots of the estimated survival curves as

well as the estimated probability of survival at years 1, 5, and 10. This analysis will be

conducted only for databases that collect data on mortality. We will also perform Kaplan-

Meier analyses to assess time from cancer diagnosis to cancer-specific survival, and other

cause-mortality-free survival. KM curves will be further stratified by study periods (to be

defined depending on data availability).

We will use joinpoint modeling to examine the overall trends in cancer incidence and survival

(to see if this can be implemented using the OMOP CDM).11 The model involves fitting a

series of joined straight lines on a logarithmic scale to the trends in the annual rates. The

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direction and magnitude of the resulting trends are described by the annual percent change

(APC), the linear slope, across each line segment between two joinpoints. The average

annual percent change (AAPC) summarizes the overall trend over time using a weighted

average of the APCs within the specified period. The default maximum number of joinpoints

allowed will be set at 4. In describing the change, the term increase or the term decrease will

be used when the APC or the AAPC is statistically significant (p< 0.05); otherwise, the term

stable was used.12

9 Sample Size and Study Power

Since this study will be undertaken using population-based data, we will include all patients

meeting the eligibility criteria described above. No prior sample calculation will be performed

but we will do feasibility counts before running the stratified analyses.

10 Strengths and Limitations

10.1 Strengths

The main strengths of this study are the sample size and the real-world nature of the data.

Our study will provide data from broadly representative and heterogeneous settings and

geographical regions. Another strength of this study is its longitudinal design including

prospectively collected data spanning up to 2021. In addition, the use of pre-existing

standardised analytics and tools across the OHDSI network will facilitate the development of

the study across different data sources.

10.2 Limitations

One potential limitation of the present study is misclassification of cancer diagnosis and/or

date of death. However, cancer diagnosis has been already validated in some of the

participating databases, and additionally, we will use the OHDSI Cohort Diagnostics tool to

assess quality of cancer incidence in the participating databases and only include high

quality data. The lack and/or missingness of information on relevant stratification factors;

such as race, smoking habits, alcohol consumption, stage of tumor at diagnosis or cancer

treatment; might also be a limitation of the study. Although we already anticipate that

stratification for these factors will not be possible in all participating databases, we will be

able to do it in a few databases that are part of the OHDSI network.

11 Protection of Human Subjects

The study uses only de-identified data. Confidentiality of patient records will be maintained at

all times. All data partners executing the study within their data sources will have received

institutional review board (IRB) approval or waiver for participation in accordance with their

institutional governance prior to execution. Data custodians will remain in full control of

executing the analysis and packaging results. The study will be executed across a federated

and distributed data network, where analysis code is sent to participating data partners and

only aggregate summary statistics are returned, with no sharing of patient-level data

between organizations. There will be no transmission of patient-level data at any time during

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these analyses. Study packages will contain minimum cell count parameters to obscure any

cells which fall below allowable reportable limits.

13 Plans for Disseminating and Communicating Study Results

Open science aims to make scientific research, including its data process and software, and

its dissemination, through publication and presentation, accessible to all levels of an

inquiring society, amateur or professional and is a governing principle of the present study.

Open science delivers reproducible, transparent and reliable evidence. All aspects of study

(except private patient data) will be open and we will actively encourage other interested

researchers, clinicians and patients to participate.

We will share the study protocol with the OHDSI community for feedback. This protocol will

link to open source code for all steps to generating the study results, which will also be made

publicly available at data.ohdsi.org.

We will deliver presentations at scientific venues including the annual OHDSI symposium.

We will also prepare scientific publications for international scientific peer-review journals.

We will publish the results of this study following the International Committee of Medical

Journal Editors (ICMJE) authorship guidelines, and will report the results following the

appropriate Strengthening the Reporting of Observational Studies in Epidemiology

(STROBE) checklist.

The main findings of this project will be shared with the general public through social media

channels. With dedicated support from the OHDSI communications specialist, we will deliver

regular press releases at key project stages, distributed via the extensive media networks of

the study partners.

References

1. International Agency for Research on Cancer, GLOBOCAN 2018 accessed via

Global Cancer Observator. Accessed March 21, 2023.

2. World Health Organization (WHO). Global Health Estimates 2020: Deaths by Cause,

Age, Sex, by Country and by Region, 2000-2019. WHO; 2020. Accessed January 10,

2022. who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-

leading-causes-of-death.

3. Gersten, O. & Barbieri, M. Evaluation of the Cancer Transition Theory in the US,

Select European Nations, and Japan by Investigating Mortality of Infectious- and

Noninfectious-Related Cancers, 1950-2018. JAMA Netw. Open 4, e215322 (2021).

4. Gersten, O. & Wilmoth, J. R. The Cancer Transition in Japan since 1951. Demogr.

Res. 7, 271–306 (2002).

5. Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence

and Mortality Worldwide for 36 Cancers in 185 Countries. CA. Cancer J. Clin. 71,

209–249 (2021).

6. Stein, C. J. & Colditz, G. A. Modifiable risk factors for cancer. Br. J. Cancer 90, 299–

303 (2004).

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7. Wu, S., Powers, S., Zhu, W. & Hannun, Y. A. Substantial contribution of extrinsic risk

factors to cancer development. Nature 529, 43–47 (2016).

8. Arnold, M. et al. Progress in cancer survival, mortality, and incidence in seven high-

income countries 1995–2014 (ICBP SURVMARK-2): a population-based study.

Lancet Oncol. 20, 1493–1505 (2019).

9. Allemani, C. et al. Global surveillance of trends in cancer survival 2000–14

(CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with

one of 18 cancers from 322 population-based registries in 71 countries. The Lancet

391, 1023–1075 (2018).

10. Srinivasan, S. et al. Small is essential: importance of subpopulation research in

cancer control. Am. J. Public Health 105 Suppl 3, S371-373 (2015).

11. Observational Health Data Sciences and Informatics. The Book of OHDSI [Internet].

2019. Accessed January 10, 2022. https://ohdsi.github.io/TheBookOfOhdsi/.

12. Kim, H. J., Fay, M. P., Feuer, E. J. & Midthune, D. N. Permutation tests for joinpoint

regression with applications to cancer rates. Stat. Med. 19, 335–351 (2000).

13. Culp, M. B., Soerjomataram, I., Efstathiou, J. A., Bray, F. & Jemal, A. Recent Global

Patterns in Prostate Cancer Incidence and Mortality Rates. Eur. Urol. 77, 38–52

(2020).

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Appendix 1. Preliminary code list of cancer

Attached as a standalone document

Appendix 2. Preliminary code list of conditions

Conditions Concept id (Included) Concept id (Excluded)

Anemia 439777 434701, 43022052

Anxiety 441542

Asthma 317009, 4235703, 4279553

Autoimmune condition 4137275, 81893, 40484648, 201254, 4063582, 134442, 257628, 254443, 4035611, 4083556, 438688, 45772123, 4262578, 2107559, 2107558, 2107560, 80809, 4145240, 46273478, 81931, 140168, 4102493, 46270482, 432295, 2108721, 4105026, 4107913, 432893, 76685, 374919, 4105005, 255304, 441928, 4331739, 46272236, 46274082, 46269999, 135215, 4058299, 4232076, 46269952, 46273369, 435216, 2107561, 2107572, 46273442, 4334806, 4297650, 201606, 46273477, 46269889, 194992, 4116142, 443394, 75614

Atrial fibrillation 313217

Cardiovascular disease 4329847, 316139, 432923, 4148906, 42535425, 43530727, 43530674, 439847, 376713, 441874, 375557, 372924, 4132309, 4317150, 442289, 321042, 4048809

314666, 315295, 443454, 437894

Cerebrovascular disease 381591

Chronic kidney disease 194385, 46271022, 192279, 4263367, 261071, 201313, 4103224, 193253, 195314, 192359, 45768812

45769152, 195289, 195737, 43530912, 37116834, 195014, 197930, 197320, 4066005

Chronic liver disease 4212540

COPD 255573, 258780

Coronary arteriosclerosis 317576

Chron’s disease 201606, 46269889, 46269999

Dementia 37312036, 37312035, 4041685, 37312031, 37312030, 35608576, 4092747, 4182210, 37311665, 4043378, 45765480, 45765477, 37311890, 37312577, 4059191

37116464, 37017549, 4244346, 377788, 372610, 37017247

Depressive disorder 440383 438727, 436665, 40481798, 435520, 4224940

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GERD 318800

GI-Bleeding 192671

Heart Failure 316139 315295

HIV 4276586, 44783356, 439727 4013105, 432554

HPV 40480043, 4084948, 4116193, 4080771, 4294441, 4291601, 619210, 760929, 4219870, 4266804, 4269876, 4084816, 44810559, 4304732, 4164483, 4129543, 4175989, 198075, 4080330, 4147672, 4078931, 4291605, 37116426, 4347555, 4177636, 4345817, 4148102, 4345473, 36713662, 37206940, 45757380, 36717114, 45757381, 441788, 4333885, 35610330, 36715556, 35610522, 4270602, 37109025, 42535207, 4084817, 4080770, 36716153, 4291602, 4306683, 4300215, 4081909, 4142828, 4185025, 4182586, 4200132, 3657814, 3657815, 4080331, 760906, 760907, 36685421, 44810378, 4302049, 36715819, 4345474, 4145196, 4028324, 137785, 140641, 40490394, 40491348, 40490302, 40489357, 4291600, 4294439, 4300214, 4294440, 4291603, 4130346, 36716496, 4296065, 3656108, 4111926, 4289145

Hyperlipidemia 432867

Hypertension 316866, 4322024, 42709887 4167493

Hyperthyroidism 140673

Ischemic heart disease 4185932

Myocardial infarction 4329847 314666

NAFLD 4026131, 40484532

Obesity 4060985, 4256640, 45766204, 433736, 4176962, 4081038

Osteoarthritis 80180

Osteoporosis 80502

Pancreatitis 4192640

Peripheral vascular disease

321052

Pneumonia 4050869, 255848 45770911, 4001167, 4049965, 36712839, 252552

Psoriasis 140168

Pulmonary embolism 440417

Renal impairment 4030518

16

Rheumatoid arthritis 80809

Stroke 42535426, 4048784, 4045735, 4031045, 761110, 372924, 4110189, 443454, 762951, 765515, 43530683, 762933, 762937, 4111714, 4108356, 45772786, 4110190, 762935, 763015, 46273649, 35610084, 46270031, 762934, 43531607, 35610085, 46270381, 4110192, 45767658, 44782773, 46270380, 37110678, 37110679, 381316, 35609033, 4046362, 4131383, 4046237, 4119140, 4043731, 439847, 4141405, 37116473, 4144154, 4111709, 4077086, 4046359, 4319146, 4043732, 4146185, 36717605, 43530727, 4148906, 43530728, 432923, 4108952, 4111708, 4142739, 4046358, 36684840

Type 1 Diabetes 36715571, 45769891, 37016767, 45763585, 4128019, 4225656, 45773688, 45773576, 45771075, 45769902, 45769903, 45769837, 35626765, 45769832, 45757674, 435216, 42538169, 42535539, 377821, 37016353, 42689695, 45769904, 43531565, 4221344, 4223303, 37017429, 765533, 37016348, 45757432, 443592, 201531, 42535540, 45757393, 45771067, 45769876, 4228112, 45757362, 3046418, 4047906, 4102018, 45757073, 439770, 4224254, 4143857, 35626069, 45757535, 37016179, 43530660, 37016180, 4225055, 45769829, 45769830, 37312218, 45768456, 45763583, 45769834, 36713094, 318712, 37018566, 4222687, 4222553, 37017431, 4063042, 43531008, 43531009, 45763584, 45757604, 200687, 45757266, 4227210, 45770986, 45771533, 45773567, 45769833, 765373, 46269764, 4143689, 45769873, 201254, 40484648, 40484649, 4152858, 443412, 4099214, 45766051, 45757507, 45769892, 37312201, 45770902, 37312200, 45757074, 4224709, 765650.

Type 2 Diabetes 4321756, 36717156, 43531588, 45769888, 4196141, 37016768, 609103, 609106, 609114, 609117,

17

602345, 45763582, 40483315, 4221495, 43531578, 43531559, 45769901, 43531566, 43531653, 43531577, 43531562, 37309630, 45769894, 43531616, 45757474, 36684827, 37018912, 443732, 43531597, 443733, 376065, 43531564, 45757280, 45769906, 4177050, 4223463, 43530690, 4222876, 37018728, 45772019, 604741, 37016349, 45770880, 201530, 4215719, 45757392, 45771064, 45757447, 45757446, 45757445, 45757444, 45757363, 45772060, 36714116, 608884, 45769875, 4130162, 45757075, 765375, 45771072, 443734, 4228443, 4140466, 45770830, 35626070, 45769905, 45757435, 609099, 609101, 43531651, 45770881, 609104, 609105, 4222415, 37162626, 45769828, 760989, 761063, 43531563, 45757450, 37312203, 37312202, 45770883, 37016354, 43530656, 609096, 609095, 45769836, 443729, 43530689, 45757278, 4221487, 4223739, 37017432, 3192767, 3191208, 3194332, 4063043, 43530685, 609116, 609119, 45770831, 45757499, 443731, 45770928, 4226121, 45769872, 45769835, 761053, 609109, 609112, 36712670, 46274058, 4142579, 45770832, 45773064, 201826, 45757508, 4230254, 4304377, 40485020, 4193704, 4200875, 4099651, 45769890, 37312205, 36712686, 45757277, 37312204, 36712687, 45757449, 43531608, 4099216, 761062

Ulcerative colitis 81893

UTI 81902

Venous thromboembolism 762047, 762148, 761444, 35616028, 35615035, 761416, 35615031, 43531681, 35616027, 35615034, 761415, 35615030, 44782746, 44782751, 762008, 760875, 765155, 762017, 762417, 762020, 765546, 762004, 44782742, 44782747, 762015, 765541, 44782748, 44782752, 762009, 760876, 765540,

18

765922, 762418, 765537, 44782767, 46270071, 762022, 44782743, 762021, 762010, 760877, 762013, 762018, 762419, 762005, 44782745, 44782744, 762026, 765156, 44782421, 764016, 44782766, 4120091, 45768439, 45768888, 762048, 45757410, 762049, 36712892, 44782762, 37109253, 40478951, 4042396, 4046884, 4133004, 4181315, 45773536, 763942, 761980, 443537, 4133975, 40480555, 4322565, 763941, 761928, 4207899, 4028057, 435565, 40481089, 4309039, 4119760, 762808, 40480461, 4124856, 4096099, 440738, 4281689, 4284538, 4309333, 4108681, 46285905, 440417, 37109911, 37016922, 43530605, 254662, 4253796, 4121618, 4119610, 46271900, 4236271, 36713113, 35615055, 4033521, 4119607, 4055089, 4327889, 320741, 439838, 4230403, 4069561, 761831, 761830, 761808, 761832, 761809, 4221821, 440750, 4176614, 761821, 761819, 444097, 761820, 761818, 4110339, 4111868, 4110343, 439314, 4109877, 4112171, 4112172, 4250765, 42538533, 44811347, 765049, 4317289, 4203836, 4175649, 4149782, 4153353, 46285904, 444247, 77310, 4189004

Viral Hepatitis 4291005

Appendix 3. Preliminary code list of medications for large-scale

characterizaton

Medications Concept id (Included) Concept id (Excluded)

Agents acting on the renin-angiotensin system

21601782

Antibacterials for systemic use 21602796

Antidepressants 21604686

Antiemetics 21600490

Antiepileptics 21604389

Anti-inflammatory and Antirheumatic products

21603933, 21602722

Antineoplastics 21601387

Antipsoriatics 21602028

19

Antithrombotic agents 21600961

Antivirals for HCV 1501761

Antivirals for HIV 21603180

Beta blocking agents 21601664

Calcium channel blockers 21601744

Diuretics 21601461

Drugs for acid related disorders 21600046

Drugs used in addictive disorders 21604816

Drugs for obstructive airway diseases 21603248

Drugs for diabetes 21600713, 21600744

Hormonal Contraceptives 21602472

Immunosuppressants 21603891

Iron preparations 21601078 21601119

Lipid modifying agents 21601853

Opioids 21604254

Psycholeptics 21604489

Psychostimulants 21604752

1

RESEARCH PROTOCOL:

Time trends in prevalence, incidence and survival

of cancer in the OHDSI Network

Version: 3.0

Date: 19th November 2024

Acknowledgement: The analysis is based in part on work from the Observational Health

Sciences and Informatics collaborative. OHDSI (http://ohdsi.org) is a multi-stakeholder,

interdisciplinary collaborative to create open-source solutions that bring out the value of

observational health data through large-scale analytics.

2

Table of contents

1 List of Abbreviations ....................................................................................................... 3

2 Responsible Parties ......................................................................................................... 3

2.1 Investigators .............................................................................................................. 3

3 Abstract ............................................................................................................................ 4

5 Milestones ........................................................................................................................ 5

6 Rationale and Background .............................................................................................. 6

7 Study Objectives .............................................................................................................. 6

8 Research Methods ........................................................................................................... 7

8.1 Study Design and Study Period................................................................................ 7

8.2 Data Sources ............................................................................................................. 7

8.3 Study Population ....................................................................................................... 7

8.4 Cancer prevalence, incidence and survival ............................................................. 8

8.5 Stratifications............................................................................................................. 8

8.6 Other variables .......................................................................................................... 8

8.7 Data quality assessment ........................................................................................... 9

8.8 Analysis...................................................................................................................... 9

9 Sample Size and Study Power ...................................................................................... 11

10 Strengths and Limitations ........................................................................................... 11

10.1 Strengths ................................................................................................................ 11

10.2 Limitations ............................................................................................................. 11

11 Protection of Human Subjects .................................................................................... 11

13 Plans for Disseminating and Communicating Study Results ................................... 12

References ........................................................................................................................ 12

Appendix 1. Preliminary code list of cancer ................................................................... 14

Appendix 2. Preliminary code list of conditions ............................................................. 14

Appendix 3. Preliminary code list of medications for large-scale characterizaton ...... 18

3

1 List of Abbreviations

AAPC Average Annual Percent Change

APC Annual Percent Change

CDM Common Data Model

COPD Chronic Obstructive Pulmonary Disease

EHDEN European Health Data and Evidence Network

HIV Human Immunodeficiency Virus

HPV Human Papillomavirus Infection

IRB Institutional Review Board

IR Incidence Rate

IRR Incidence Rate Ratio

OMOP Observational Medical Outcomes Partnership

OHDSI Observational Health Data Science and Informatics

PR Prevalence Rate

SIDIAP Information System for Reseach in Primary Care

SNOMED Systematized Nomenclature of Medicine

WHO World Health Organization

2 Responsible Parties

2.1 Investigators

Investigator/Author Institution/Affiliation

Talita Duarte-Salles* Fundació Institut Universitari per a la recerca a l'Atenció

Primària de Salut Jordi Gol i Gurina (IDIAPJGol),

Barcelona, Spain

Department of Medical Informatics, Erasmus University

Medical Center, Rotterdam, The Netherlands

4

Edward Burn University of Oxford

Asieh Golozar Odysseus Data Services, Inc, Cambridge, MA USA,

OHDSI Center at the Northeastern University, Boston,

MA USA

Irene López Sánchez Fundació Institut Universitari per a la recerca a l'Atenció

Primària de Salut Jordi Gol i Gurina (IDIAPJGol),

Barcelona, Spain

Laura Pérez Crespo Fundació Institut Universitari per a la recerca a l'Atenció

Primària de Salut Jordi Gol i Gurina (IDIAPJGol),

Barcelona, Spain

Agustina Giuliodori Picco Fundació Institut Universitari per a la recerca a l'Atenció

Primària de Salut Jordi Gol i Gurina (IDIAPJGol),

Barcelona, Spain

Berta Raventós Department of Medical Informatics, Erasmus University

Medical Center, Rotterdam, The Netherlands

Anna Palomar Cros Fundació Institut Universitari per a la recerca a l'Atenció

Primària de Salut Jordi Gol i Gurina (IDIAPJGol),

Barcelona, Spain

Anton Barchuk Department of Medical Informatics, Erasmus University

Medical Center, Rotterdam, The Netherlands

OHDSI Oncology WG

*Principal Investigator

Authorship in scientific manuscripts will follow ICMJE authorship criteria

(http://www.icmje.org/recommendations/browse/roles-and-responsibilities/defining-the-role-

of-authors-and-contributors.html). The Responsible Parties involved in this protocol take

accountability for the overarching protocol, package development, providing assistance to

sites running the analysis and ensuring site-specific governance is adhered to in all

publications generated from this protocol.

3 Abstract

Objectives: The main aim of this project is to estimate time trends in prevalence and

incidence rates, and short- and long-term survival of site-specific cancers in the OHDSI

network.

Design: This study will be a multinational observational cohort study and will be conducted

using a network of large real world data sources that have been mapped to the

Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM).

5

Setting: Population-based, electronic health records, claims and registry data from primary

and secondary care.

Participants: Individuals with no prior history of cancer (for incidence and survival analyses

only), and who have been on the database for at least 1 year before study entry.

Outcomes: Prevalent and incident cancer diagnoses and overall as well as 1-, 5-, and 10-

year survival of site-specific cancers.

Data analyses: The OHDSI Cohort Diagnostics package will be used to assess the fitness

of use of cancer data on each database. We will calculate prevalence (PR) and incidence

rates (IR) with 95% confidence intervals (95%CI) for each year and study period by dividing

the number of ever and first recorded cases of cancer, respectively, by 1,000 person-years

of follow-up, overall and stratified by demographics and relevant comorbidities. The overall

and 1-, 5-, and -10-year survival rates will be calculated as the percentage of people who

have been diagnosed with cancer and are still alive during the study period as well as one or

five years after diagnosis, respectively, per year and stratified by pre-defined subgroups. To

assess the incidence trend over time, we will calculate the IRs in 5 year periods and then

calculate the incidence rate ratios (IRRs) and their corresponding 95%CI to analyze the

differences in incidence between the defined time periods.

4 Amendments and Updates

Number Date Section of

study

protocol

Amendment or

update

Reason

Version 2.0 13/06/2023 All Update Update to incorporate

team comments and

suggestions

Version 3.0 19/11/2024 All Amendment Amendment to

incorporate a new

objective to the study

5 Milestones

Milestone Planned date

Final version of study protocol 20/11/2024

Create phenotype definitions 30/11/2024

Study package release 30/01/2025

6

Data analyses 01/03/2025

Writing of scientific paper(s) 01/06/2025

6 Rationale and Background

Cancer is currently a leading cause of morbidity and mortality worldwide. Estimates from the

World Health Organization (WHO) in 2019 showed that cancer is the first or second leading

cause of death before the age of 70 years in 112 of 183 countries and ranks third or fourth in

a further 23 countries.1 The burden of cancer incidence and mortality is rapidly growing

worldwide, reflecting both aging and growth of the population as well as changes in the

prevalence and distribution of the main risk factors for cancer.2,3 Worldwide, female breast

cancer is the most commonly diagnosed cancer, followed by lung, colorectal, prostate, and

stomach cancers, while lung cancer is the leading cause of cancer death, followed by

colorectal, liver, stomach, and female breast cancers.4 The continuous surveillance and

monitoring of trends in cancer incidence and survival are needed for the development,

implementation and evaluation of health policies aiming to reduce the burden of disease.

Multiple factors are attributed to cancer incidence and cancer survival. Some of the main

cancer risk factors include age, race, smoking, alcohol consumption, cardiometabolic

conditions (e.g.: cardiovascular diseases, obesity, hypertension, type 2 diabetes mellitus),

infectious diseases (e.g.: hepatitis B/C, human immunodeficiency virus (HIV)), among

others. 5,6 Survival rates are known to vary by cancer type as well as being influenced by

cancer stage at detection and treatment. It can also depend on an individual’s health,

presence of comorbidities and other tumour-related factors. Recent advances in screening

programmes and treatments have improved the survival rates for some cancer sites. 7

However, important disparities still exist in healthcare systems and countries. 7,8

Understanding the complexities of individual factors related to cancer incidence and survival,

whilst investigating the population subgroups that are more prone to be at risk, is important

for being able to plan future population-based interventions. 9

Real world data from healthcare services promise to vastly expand clinical research,

providing data for large-scale studies that would not be feasible with traditional research data

collection methods. The Observational Health Data Sciences and Informatics (OHDSI;

www.ohdsi.org) is a multi-stakeholder and interdisciplinary international network that

generates open science through large-scale analytics of real world data. 10 This data network

offers a unique opportunity to study cancer incidence and survival as it includes more than

100 healthcare databases from 20 countries including over 1.5 billion individual records with

longitudinal data. OHDSI analyses are based around the use of the Observational Medical

Outcomes Partnership (OMOP) Common Data Model (CDM), which is used to standardize

otherwise disparate datasets. In this project, we will estimate time trends in prevalence,

incidence rates and survival of cancers in the OHDSI network.

7 Study Objectives

7

The main aim of this project is to estimate time trends in prevalence, incidence rates and

short- and long-term survival of site-specific cancers in the OHDSI network.

The specific study objectives include:

1) To estimate prevalence rates of site-specific cancers by calendar year, age, sex, and

comorbidities.

2) To estimate incidence rates of site-specific cancers by calendar year, age, sex, and

comorbidities.

3) To describe demographic, clinical characteristics and medications of individuals with

cancer at the time of diagnosis.

4) To estimate overall, short- and long-term survival of site-specific cancers by calendar

year, age, sex, and comorbidities.

8 Research Methods

8.1 Study Design and Study Period

This study will be a multinational observational cohort study. The study period will start on

1st January 2000 or one year after the earliest date of available data in each database and

span up to the 31st December 2024 (or the last date of available data in each database).

8.2 Data Sources

The study will be conducted using a network of large real world data sources that have been

mapped to the OMOP Common Data Model in collaboration with the OHDSI and European

Health Data and Evidence Network (EHDEN) initiatives. The OMOP Common Data Model

(https://github.com/OHDSI/CommonDataModel/wiki) includes a standard representation of

health care experiences (such as information related to drug utilization and condition

occurrence), as well as common vocabularies for coding clinical concepts, and enables

consistent application of analyses across multiple disparate data sources.10 The present

study will be conducted in multiple databases in the OHDSI network willing to participate.

Databases that have already committed to participate include: the Information System for

Research in Primary Care (SIDIAP; Spain), the Integrated Primary Care Information (IPCI,

The Netherlands), The Netherlands Cancer Registry (IKNL, The Netherlands), the Clinical

Practice Research Datalink (CPRD, UK).

8.3 Study Population

All individuals registered in each of the data sources will be eligible for inclusion in the study.

However, study participants will be required to have a year of prior history observed in the

database before contributing observation time. Participants with prior history of a diagnosis

of cancer (any, excluding non-melanoma skin cancer) any time prior to their index date will

be excluded (for incidence and survival estimation).

For incidence rates, study participants will begin contributing person time on the respective

date of the latest of the following: 1) study start date (1st January 2000 or the earliest date of

available data in in each of the data sources), 2) date at which they have sufficient prior

8

history (defined as 365 days), 3) date at which they reach a minimum age (where age strata

are being considered).

Participants will stop contributing person time at the earliest date of the following: 1) study

end date (end of available data in each of the data sources), 2) date at which their

observation period ends, 3) the last day in which they have the maximum age (where age

strata are being considered). Where there are multiple age strata, study participants will

contribute to each strata while they satisfy the conditions of that strata (i.e. when they reach

the limit of one age strata they will begin contributing to the next).

8.4 Cancer prevalence, incidence and survival

Cancer prevalence and incidence will be defined as the occurrence for ever or the first time

in the person’s history, respectively. Conditions in the OMOP CDM use the Systematized

Nomenclature of Medicine (SNOMED) as the standard vocabulary for diagnosis codes. The

complete list of codes used to define each cancer site are available in Appendix 1.

For those databases with available data on date and/or cause of death, overall, 1-, 5-, and

10-year survival will be estimated. We will also report overall mortality, cancer-specific

mortality, and other-cause mortality when possible.

8.5 Stratifications

Each target cohort will be analysed in full and stratified on sex (male, female), age (0-9; 10-

19; 20-29; 30-39; 40-49; 50-59; 60-69; 70-79; 80-89; 90-99; 100 and over), smoking status,

asthma, type 2 diabetes mellitus, hypertension, cardiovascular diseases, chronic obstructive

pulmonary disease (COPD), obesity, human immunodeficiency virus (HIV), human

papillomavirus infection (HPV), viral hepatitis, chronic kidney disease, autoimmune

conditions, and depression. These stratifications will only be performed in those databases

with available information on the variables listed above. The definition of these conditions

can be found in Appendix 2. Female breast cancer will be stratified by menopausal status

which will be defined based on age as pre- (less than 50 years) and post-menopausal (50

years or above). All stratum are pending meeting minimum reportable cell counts (as

specified by data owners).

8.6 Other variables

Large-scale patient-level characterization will be conducted at the time of diagnosis. Medical

history and medications use history will be assessed at any time prior cancer diagnosis and

up to 366 days before index date, for 365 days to 31 days before index date, for 30 to 1 day

before index date and at index date. We will also report medication use for 1 to 90 days post

index-date.

A list of pre-specified comorbidities and medications will be described. These will include:

• Medical History: Anemia, Anxiety, Asthma, Arterial fibrillation, Cerebrovascular disease,

Chronic kidney disease, Chronic liver disease, Chronic obstructive pulmonary disease

(COPD), Coronary arteriosclerosis, Chron’s Disease, Dementia, Depressive disorder,

Gastro-oesophageal reflux disease (GERD), GI-Bleeding, Heart failure, Human

9

Immunodeficiency Virus (HIV), Human papillomavirus infection (HPV), Hyperlipidaemia,

Hypertension, Hypothyroidism, Ischemic Heart Disease, Myocardial Infraction, Non-

alcoholic Fatty-acid disease (NAFLD), Obesity, Osteoarthritis, Osteoporosis,

Pancreatitis, Peripheral vascular disease, Pneumonia, Psoriasis, Pulmonary embolism,

Renal impairment, Stroke, Type 1 Diabetes, Type 2 Diabetes, Ulcerative colitis, UTI,

Venous thromboembolism, Viral Hepatitis

• Medications: Agents acting on the renin-angiotensin system, Antibacterials for systemic

use, Antidepressants, Antiemetics, Antiepileptics, Anti-inflammatory and anti-rheumatic

drugs, Antineoplastics, Anti-psoriatic, Antithrombotic agents, Antivirals HCV, antivirals

HIV, Beta-blocking agents, Calcium channel blockers, Contraceptives, Diuretics, Drugs

for acid related disorders, Drugs for obstructive airway diseases, Drugs used in diabetes,

Immunosuppressants, Iron preparations, Lipid-modifying agents, Opioids, Psycholeptics

and Psychostimulants.

8.7 Data quality assessment

Overall data quality of the OHDSI Network databases are assessed by each data partner

using the data quality dashboard after data mapping. 10 We will assess the created

phenotypes including cancer events using a comprehensive cohort characterization tool, the

OHDSI’s CohortDiagnostic package (https://ohdsi.github.io/CohortDiagnostics/). For any

cohort and data source mapped to OMOP CDM, this package systematically generates

incidence rates (stratified by age, gender, calendar year, and database), cohort

characteristics (all comorbidities, drug use, procedures, health utilization) and the actual

codes found in the data triggering the various rules in the cohort definitions. The

CohortDiagnostics package works in two steps: 1) Generate the utilization results and

diagnostics against a data source and 2) Explore the generated utilization and diagnostics in

a user-friendly graphical interface R-Shiny app. These diagnostics provide a consistent

methodology to evaluate cohort definitions/phenotype algorithms across a variety of

observational databases, allowing to compare the overlap between alternative definitions.

This can allow researchers and stakeholders to understand the heterogeneity of source

coding for exposures and health outcomes as well as the impact of various inclusion criteria

on overall cohort counts. Data quality of cancer diagnoses will be assessed by comparing

the calculated incidence rates of events in a specific database with the incidence reported in

national/regional cancer registries.

8.8 Analysis

All analyses will be performed using code developed for the OHDSI Methods library and

executed by data owners using R and stratified by database. Each data partner will execute

the study code against their database containing patient-level data and will then return the

results set which will only contain aggregated data. The results from each of the contributing

data sites will then be combined in tables and figures. The code for this study will be made

publicly available at https://github.com/ohdsi-studies/.

To answer the study objectives, a diagnostic package, built off the OHDSI Cohort

Diagnostics (https://ohdsi.github.io/CohortDiagnostics/) library, will be included in the base

package as a preliminary step to assess the fitness of use of phenotypes on each database.

10

If a database passes cohort diagnostics (as described in section 8.6), the full study package

will be executed.

To address objectives 1 and 2, we will first summarize the socio-demographics

characteristics and overall follow-up of individuals included in the study, with counts and

percentages for categorical variables and median and interquartile ranges (IQR) for

continuous variables. Incidence rates (IR) with 95% CI will be calculated using the R

package IncidencePrevalence (https://cran.r-

project.org/web/packages/IncidencePrevalence/index.html). IR will be computed for each year

and study period by dividing first-recorded diagnoses by person-years at risk for the whole

population or pre-defined subgroups (section 8.5). The first recorded for each outcome

category will be included as an incident episode. Each outcome will be assessed separately

and therefore, individuals will be able to contribute to one the incidence counts for more than

one outcome. To exclude prevalent cases from incidence calculations, individuals diagnosed

with any cancer will not be considered as eligible incident cases in future cohorts after the

date of their first diagnosis. To assess the incidence trend over time, we will calculate the

IRs in 5 year periods and then calculated the incidence rate ratios (IRRs) and their

corresponding 95% confidence intervals (95% CIs) to analyze the differences in incidence

between the defined time periods (to be defined depending on data availability).

We will conduct a large-scale patient-level characterization to address objective 3. For this

part of the analysis, we will use the package CohortCharacteristics (https://cran.r-

project.org/web/packages/CohortCharacteristics/index.html). Essentially, we will use the

function summariseLargeScaleCharacteristics, which is used to summarize the large-scale

characteristics of a cohort table. Medical history and medications will be assessed for any

time prior cancer diagnosis and up to 366 days before index date, for 365 days to 31 days

before index date, for 30 to 1 day before index date and at index date (see appendix 2 and

3). Medications will also be assessed 1 to 90 days post-index date. We will also compare

these characteristics in our cohorts of interest to the proportion of these diseases in a

random subsample of SIDIAP.

To address objective 4, the 1-, 5-, and 10-year survival rates will be calculated as the

percentage of people who have been diagnosed with cancer and are still alive one, five, or

ten years after diagnosis, respectively, per year, overall and also stratified by pre-defined

subgroups.

Survival will be calculated using data on time at risk of death from any cause and the

Kaplan-Meier method. Results will be reported as plots of the estimated survival curves as

well as the estimated probability of survival at years 1, 5, and 10. This analysis will be

conducted only for databases that collect data on mortality. We will also perform Kaplan-

Meier analyses to assess time from cancer diagnosis to cancer-specific survival, and other

cause-mortality-free survival. KM curves will be further stratified by study periods (to be

defined depending on data availability).

We will use joinpoint modeling to examine the overall trends in cancer incidence and survival

(to see if this can be implemented using the OMOP CDM).11 The model involves fitting a

series of joined straight lines on a logarithmic scale to the trends in the annual rates. The

11

direction and magnitude of the resulting trends are described by the annual percent change

(APC), the linear slope, across each line segment between two joinpoints. The average

annual percent change (AAPC) summarizes the overall trend over time using a weighted

average of the APCs within the specified period. The default maximum number of joinpoints

allowed will be set at 4. In describing the change, the term increase or the term decrease will

be used when the APC or the AAPC is statistically significant (p< 0.05); otherwise, the term

stable was used.12

9 Sample Size and Study Power

Since this study will be undertaken using population-based data, we will include all patients

meeting the eligibility criteria described above. No prior sample calculation will be performed

but we will do feasibility counts before running the stratified analyses.

10 Strengths and Limitations

10.1 Strengths

The main strengths of this study are the sample size and the real-world nature of the data.

Our study will provide data from broadly representative and heterogeneous settings and

geographical regions. Another strength of this study is its longitudinal design including

prospectively collected data spanning up to 2021. In addition, the use of pre-existing

standardised analytics and tools across the OHDSI network will facilitate the development of

the study across different data sources.

10.2 Limitations

One potential limitation of the present study is misclassification of cancer diagnosis and/or

date of death. However, cancer diagnosis has been already validated in some of the

participating databases, and additionally, we will use the OHDSI Cohort Diagnostics tool to

assess quality of cancer incidence in the participating databases and only include high

quality data. The lack and/or missingness of information on relevant stratification factors;

such as race, smoking habits, alcohol consumption, stage of tumor at diagnosis or cancer

treatment; might also be a limitation of the study. Although we already anticipate that

stratification for these factors will not be possible in all participating databases, we will be

able to do it in a few databases that are part of the OHDSI network.

11 Protection of Human Subjects

The study uses only de-identified data. Confidentiality of patient records will be maintained at

all times. All data partners executing the study within their data sources will have received

institutional review board (IRB) approval or waiver for participation in accordance with their

institutional governance prior to execution. Data custodians will remain in full control of

executing the analysis and packaging results. The study will be executed across a federated

and distributed data network, where analysis code is sent to participating data partners and

only aggregate summary statistics are returned, with no sharing of patient-level data

between organizations. There will be no transmission of patient-level data at any time during

12

these analyses. Study packages will contain minimum cell count parameters to obscure any

cells which fall below allowable reportable limits.

13 Plans for Disseminating and Communicating Study Results

Open science aims to make scientific research, including its data process and software, and

its dissemination, through publication and presentation, accessible to all levels of an

inquiring society, amateur or professional and is a governing principle of the present study.

Open science delivers reproducible, transparent and reliable evidence. All aspects of study

(except private patient data) will be open and we will actively encourage other interested

researchers, clinicians and patients to participate.

We will share the study protocol with the OHDSI community for feedback. This protocol will

link to open source code for all steps to generating the study results, which will also be made

publicly available at data.ohdsi.org.

We will deliver presentations at scientific venues including the annual OHDSI symposium.

We will also prepare scientific publications for international scientific peer-review journals.

We will publish the results of this study following the International Committee of Medical

Journal Editors (ICMJE) authorship guidelines, and will report the results following the

appropriate Strengthening the Reporting of Observational Studies in Epidemiology

(STROBE) checklist.

The main findings of this project will be shared with the general public through social media

channels. With dedicated support from the OHDSI communications specialist, we will deliver

regular press releases at key project stages, distributed via the extensive media networks of

the study partners.

References

1. International Agency for Research on Cancer, GLOBOCAN 2018 accessed via

Global Cancer Observator. Accessed March 21, 2023.

2. World Health Organization (WHO). Global Health Estimates 2020: Deaths by Cause,

Age, Sex, by Country and by Region, 2000-2019. WHO; 2020. Accessed January 10,

2022. who.int/data/gho/data/themes/mortality-and-global-health-estimates/ghe-

leading-causes-of-death.

3. Gersten, O. & Barbieri, M. Evaluation of the Cancer Transition Theory in the US,

Select European Nations, and Japan by Investigating Mortality of Infectious- and

Noninfectious-Related Cancers, 1950-2018. JAMA Netw. Open 4, e215322 (2021).

4. Gersten, O. & Wilmoth, J. R. The Cancer Transition in Japan since 1951. Demogr.

Res. 7, 271–306 (2002).

5. Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence

and Mortality Worldwide for 36 Cancers in 185 Countries. CA. Cancer J. Clin. 71,

209–249 (2021).

6. Stein, C. J. & Colditz, G. A. Modifiable risk factors for cancer. Br. J. Cancer 90, 299–

303 (2004).

13

7. Wu, S., Powers, S., Zhu, W. & Hannun, Y. A. Substantial contribution of extrinsic risk

factors to cancer development. Nature 529, 43–47 (2016).

8. Arnold, M. et al. Progress in cancer survival, mortality, and incidence in seven high-

income countries 1995–2014 (ICBP SURVMARK-2): a population-based study.

Lancet Oncol. 20, 1493–1505 (2019).

9. Allemani, C. et al. Global surveillance of trends in cancer survival 2000–14

(CONCORD-3): analysis of individual records for 37 513 025 patients diagnosed with

one of 18 cancers from 322 population-based registries in 71 countries. The Lancet

391, 1023–1075 (2018).

10. Srinivasan, S. et al. Small is essential: importance of subpopulation research in

cancer control. Am. J. Public Health 105 Suppl 3, S371-373 (2015).

11. Observational Health Data Sciences and Informatics. The Book of OHDSI [Internet].

2019. Accessed January 10, 2022. https://ohdsi.github.io/TheBookOfOhdsi/.

12. Kim, H. J., Fay, M. P., Feuer, E. J. & Midthune, D. N. Permutation tests for joinpoint

regression with applications to cancer rates. Stat. Med. 19, 335–351 (2000).

13. Culp, M. B., Soerjomataram, I., Efstathiou, J. A., Bray, F. & Jemal, A. Recent Global

Patterns in Prostate Cancer Incidence and Mortality Rates. Eur. Urol. 77, 38–52

(2020).

14

Appendix 1. Preliminary code list of cancer

Attached as a standalone document

Appendix 2. Preliminary code list of conditions

Conditions Concept id (Included) Concept id (Excluded)

Anemia 439777 434701, 43022052

Anxiety 441542

Asthma 317009, 4235703, 4279553

Autoimmune condition 4137275, 81893, 40484648, 201254, 4063582, 134442, 257628, 254443, 4035611, 4083556, 438688, 45772123, 4262578, 2107559, 2107558, 2107560, 80809, 4145240, 46273478, 81931, 140168, 4102493, 46270482, 432295, 2108721, 4105026, 4107913, 432893, 76685, 374919, 4105005, 255304, 441928, 4331739, 46272236, 46274082, 46269999, 135215, 4058299, 4232076, 46269952, 46273369, 435216, 2107561, 2107572, 46273442, 4334806, 4297650, 201606, 46273477, 46269889, 194992, 4116142, 443394, 75614

Atrial fibrillation 313217

Cardiovascular disease 4329847, 316139, 432923, 4148906, 42535425, 43530727, 43530674, 439847, 376713, 441874, 375557, 372924, 4132309, 4317150, 442289, 321042, 4048809

314666, 315295, 443454, 437894

Cerebrovascular disease 381591

Chronic kidney disease 194385, 46271022, 192279, 4263367, 261071, 201313, 4103224, 193253, 195314, 192359, 45768812

45769152, 195289, 195737, 43530912, 37116834, 195014, 197930, 197320, 4066005

Chronic liver disease 4212540

COPD 255573, 258780

Coronary arteriosclerosis 317576

Chron’s disease 201606, 46269889, 46269999

Dementia 37312036, 37312035, 4041685, 37312031, 37312030, 35608576, 4092747, 4182210, 37311665, 4043378, 45765480, 45765477, 37311890, 37312577, 4059191

37116464, 37017549, 4244346, 377788, 372610, 37017247

Depressive disorder 440383 438727, 436665, 40481798, 435520, 4224940

15

GERD 318800

GI-Bleeding 192671

Heart Failure 316139 315295

HIV 4276586, 44783356, 439727 4013105, 432554

HPV 40480043, 4084948, 4116193, 4080771, 4294441, 4291601, 619210, 760929, 4219870, 4266804, 4269876, 4084816, 44810559, 4304732, 4164483, 4129543, 4175989, 198075, 4080330, 4147672, 4078931, 4291605, 37116426, 4347555, 4177636, 4345817, 4148102, 4345473, 36713662, 37206940, 45757380, 36717114, 45757381, 441788, 4333885, 35610330, 36715556, 35610522, 4270602, 37109025, 42535207, 4084817, 4080770, 36716153, 4291602, 4306683, 4300215, 4081909, 4142828, 4185025, 4182586, 4200132, 3657814, 3657815, 4080331, 760906, 760907, 36685421, 44810378, 4302049, 36715819, 4345474, 4145196, 4028324, 137785, 140641, 40490394, 40491348, 40490302, 40489357, 4291600, 4294439, 4300214, 4294440, 4291603, 4130346, 36716496, 4296065, 3656108, 4111926, 4289145

Hyperlipidemia 432867

Hypertension 316866, 4322024, 42709887 4167493

Hyperthyroidism 140673

Ischemic heart disease 4185932

Myocardial infarction 4329847 314666

NAFLD 4026131, 40484532

Obesity 4060985, 4256640, 45766204, 433736, 4176962, 4081038

Osteoarthritis 80180

Osteoporosis 80502

Pancreatitis 4192640

Peripheral vascular disease

321052

Pneumonia 4050869, 255848 45770911, 4001167, 4049965, 36712839, 252552

Psoriasis 140168

Pulmonary embolism 440417

Renal impairment 4030518

16

Rheumatoid arthritis 80809

Stroke 42535426, 4048784, 4045735, 4031045, 761110, 372924, 4110189, 443454, 762951, 765515, 43530683, 762933, 762937, 4111714, 4108356, 45772786, 4110190, 762935, 763015, 46273649, 35610084, 46270031, 762934, 43531607, 35610085, 46270381, 4110192, 45767658, 44782773, 46270380, 37110678, 37110679, 381316, 35609033, 4046362, 4131383, 4046237, 4119140, 4043731, 439847, 4141405, 37116473, 4144154, 4111709, 4077086, 4046359, 4319146, 4043732, 4146185, 36717605, 43530727, 4148906, 43530728, 432923, 4108952, 4111708, 4142739, 4046358, 36684840

Type 1 Diabetes 36715571, 45769891, 37016767, 45763585, 4128019, 4225656, 45773688, 45773576, 45771075, 45769902, 45769903, 45769837, 35626765, 45769832, 45757674, 435216, 42538169, 42535539, 377821, 37016353, 42689695, 45769904, 43531565, 4221344, 4223303, 37017429, 765533, 37016348, 45757432, 443592, 201531, 42535540, 45757393, 45771067, 45769876, 4228112, 45757362, 3046418, 4047906, 4102018, 45757073, 439770, 4224254, 4143857, 35626069, 45757535, 37016179, 43530660, 37016180, 4225055, 45769829, 45769830, 37312218, 45768456, 45763583, 45769834, 36713094, 318712, 37018566, 4222687, 4222553, 37017431, 4063042, 43531008, 43531009, 45763584, 45757604, 200687, 45757266, 4227210, 45770986, 45771533, 45773567, 45769833, 765373, 46269764, 4143689, 45769873, 201254, 40484648, 40484649, 4152858, 443412, 4099214, 45766051, 45757507, 45769892, 37312201, 45770902, 37312200, 45757074, 4224709, 765650.

Type 2 Diabetes 4321756, 36717156, 43531588, 45769888, 4196141, 37016768, 609103, 609106, 609114, 609117,

17

602345, 45763582, 40483315, 4221495, 43531578, 43531559, 45769901, 43531566, 43531653, 43531577, 43531562, 37309630, 45769894, 43531616, 45757474, 36684827, 37018912, 443732, 43531597, 443733, 376065, 43531564, 45757280, 45769906, 4177050, 4223463, 43530690, 4222876, 37018728, 45772019, 604741, 37016349, 45770880, 201530, 4215719, 45757392, 45771064, 45757447, 45757446, 45757445, 45757444, 45757363, 45772060, 36714116, 608884, 45769875, 4130162, 45757075, 765375, 45771072, 443734, 4228443, 4140466, 45770830, 35626070, 45769905, 45757435, 609099, 609101, 43531651, 45770881, 609104, 609105, 4222415, 37162626, 45769828, 760989, 761063, 43531563, 45757450, 37312203, 37312202, 45770883, 37016354, 43530656, 609096, 609095, 45769836, 443729, 43530689, 45757278, 4221487, 4223739, 37017432, 3192767, 3191208, 3194332, 4063043, 43530685, 609116, 609119, 45770831, 45757499, 443731, 45770928, 4226121, 45769872, 45769835, 761053, 609109, 609112, 36712670, 46274058, 4142579, 45770832, 45773064, 201826, 45757508, 4230254, 4304377, 40485020, 4193704, 4200875, 4099651, 45769890, 37312205, 36712686, 45757277, 37312204, 36712687, 45757449, 43531608, 4099216, 761062

Ulcerative colitis 81893

UTI 81902

Venous thromboembolism 762047, 762148, 761444, 35616028, 35615035, 761416, 35615031, 43531681, 35616027, 35615034, 761415, 35615030, 44782746, 44782751, 762008, 760875, 765155, 762017, 762417, 762020, 765546, 762004, 44782742, 44782747, 762015, 765541, 44782748, 44782752, 762009, 760876, 765540,

18

765922, 762418, 765537, 44782767, 46270071, 762022, 44782743, 762021, 762010, 760877, 762013, 762018, 762419, 762005, 44782745, 44782744, 762026, 765156, 44782421, 764016, 44782766, 4120091, 45768439, 45768888, 762048, 45757410, 762049, 36712892, 44782762, 37109253, 40478951, 4042396, 4046884, 4133004, 4181315, 45773536, 763942, 761980, 443537, 4133975, 40480555, 4322565, 763941, 761928, 4207899, 4028057, 435565, 40481089, 4309039, 4119760, 762808, 40480461, 4124856, 4096099, 440738, 4281689, 4284538, 4309333, 4108681, 46285905, 440417, 37109911, 37016922, 43530605, 254662, 4253796, 4121618, 4119610, 46271900, 4236271, 36713113, 35615055, 4033521, 4119607, 4055089, 4327889, 320741, 439838, 4230403, 4069561, 761831, 761830, 761808, 761832, 761809, 4221821, 440750, 4176614, 761821, 761819, 444097, 761820, 761818, 4110339, 4111868, 4110343, 439314, 4109877, 4112171, 4112172, 4250765, 42538533, 44811347, 765049, 4317289, 4203836, 4175649, 4149782, 4153353, 46285904, 444247, 77310, 4189004

Viral Hepatitis 4291005

Appendix 3. Preliminary code list of medications for large-scale

characterizaton

Medications Concept id (Included) Concept id (Excluded)

Agents acting on the renin-angiotensin system

21601782

Antibacterials for systemic use 21602796

Antidepressants 21604686

Antiemetics 21600490

Antiepileptics 21604389

Anti-inflammatory and Antirheumatic products

21603933, 21602722

Antineoplastics 21601387

Antipsoriatics 21602028

19

Antithrombotic agents 21600961

Antivirals for HCV 1501761

Antivirals for HIV 21603180

Beta blocking agents 21601664

Calcium channel blockers 21601744

Diuretics 21601461

Drugs for acid related disorders 21600046

Drugs used in addictive disorders 21604816

Drugs for obstructive airway diseases 21603248

Drugs for diabetes 21600713, 21600744

Hormonal Contraceptives 21602472

Immunosuppressants 21603891

Iron preparations 21601078 21601119

Lipid modifying agents 21601853

Opioids 21604254

Psycholeptics 21604489

Psychostimulants 21604752