Data sharing practices of medicines related apps and the mobile ecosystem: traffic, content, and network analysisBMJ 2019; 364 doi: https://doi.org/10.1136/bmj.l920 (Published 20 March 2019) Cite this as: BMJ 2019;364:l920
- Quinn Grundy, assistant professor and honorary senior lecturer12,
- Kellia Chiu, PhD candidate2,
- Fabian Held, senior research fellow2,
- Andrea Continella, postdoctoral fellow3,
- Lisa Bero, professor2,
- Ralph Holz, lecturer in networks and security4
- 1Faculty of Nursing, University of Toronto, Suite 130, 155 College St, Toronto, ON, Canada, M5T 1P8
- 2School of Pharmacy, Charles Perkins Centre, The University of Sydney, Sydney, NSW, Australia
- 3Department of Computer Science, University of California, Santa Barbara, CA, USA
- 4School of Computer Science, The University of Sydney, Sydney, NSW, Australia
- Correspondence to: Q Grundy @quinngrundy on Twitter) (or
- Accepted 25 February 2019
Objectives To investigate whether and how user data are shared by top rated medicines related mobile applications (apps) and to characterise privacy risks to app users, both clinicians and consumers.
Design Traffic, content, and network analysis.
Setting Top rated medicines related apps for the Android mobile platform available in the Medical store category of Google Play in the United Kingdom, United States, Canada, and Australia.
Participants 24 of 821 apps identified by an app store crawling program. Included apps pertained to medicines information, dispensing, administration, prescribing, or use, and were interactive.
Interventions Laboratory based traffic analysis of each app downloaded onto a smartphone, simulating real world use with four dummy scripts. The app’s baseline traffic related to 28 different types of user data was observed. To identify privacy leaks, one source of user data was modified and deviations in the resulting traffic observed.
Main outcome measures Identities and characterisation of entities directly receiving user data from sampled apps. Secondary content analysis of company websites and privacy policies identified data recipients’ main activities; network analysis characterised their data sharing relations.
Results 19/24 (79%) of sampled apps shared user data. 55 unique entities, owned by 46 parent companies, received or processed app user data, including developers and parent companies (first parties) and service providers (third parties). 18 (33%) provided infrastructure related services such as cloud services. 37 (67%) provided services related to the collection and analysis of user data, including analytics or advertising, suggesting heightened privacy risks. Network analysis revealed that first and third parties received a median of 3 (interquartile range 1-6, range 1-24) unique transmissions of user data. Third parties advertised the ability to share user data with 216 “fourth parties”; within this network (n=237), entities had access to a median of 3 (interquartile range 1-11, range 1-140) unique transmissions of user data. Several companies occupied central positions within the network with the ability to aggregate and re-identify user data.
Conclusions Sharing of user data is routine, yet far from transparent. Clinicians should be conscious of privacy risks in their own use of apps and, when recommending apps, explain the potential for loss of privacy as part of informed consent. Privacy regulation should emphasise the accountabilities of those who control and process user data. Developers should disclose all data sharing practices and allow users to choose precisely what data are shared and with whom.
Mobile health apps are a booming market targeted at both patients and health professionals.2 These apps claim to offer tailored and cost effective health promotion, but they pose unprecedented risk to consumers’ privacy given their ability to collect user data, including sensitive information. Health app developers routinely, and legally, share consumer data with third parties in exchange for services that enhance the user’s experience (eg, connecting to social media) or to monetise the app (eg, hosted advertisements).34 Little transparency exists around third party data sharing, and health apps routinely fail to provide privacy assurances, despite collecting and transmitting multiple forms of personal and identifying information.56789
Third parties may collate data on an individual from multiple sources. Threats to privacy are heightened when data are aggregated across multiple sources and consumers have no way to identify whether the apps or websites they use share their data with the same third party providers.3 Collated data are used to populate proprietary algorithms that promise to deliver “insights” into consumers. Thus, the sharing of user data ultimately has real world consequences in the form of highly targeted advertising or algorithmic decisions about insurance premiums, employability, financial services, or suitability for housing. These decisions may be discriminatory or made on the basis of incomplete or inaccurate data, with little recourse for consumers.1011
Apps that provide medicines related information and services may be particularly likely to share or sell data, given that these apps collect sensitive, specific medical information of high value to third parties.12 For example, drug information and clinical decision support apps that target health professionals are of particular interest to pharmaceutical companies, which can offer tailored advertising and glean insights into prescribing habits.13 Drug adherence apps targeting consumers can deliver a detailed account of a patient’s health history and behaviours related to the use of medicines.14
We investigated the nature of data transmission to third parties among top rated medicines related apps, including the type of consumer data and the number and identities of third parties, and we characterised the relations among third parties to whom consumer data are transmitted.
We carried out this study in two phases: the first was a traffic analysis of the data sharing practices of the apps and the second was a content and network analysis to characterise third parties and their interrelations (box 1).
Box 1 Description of methods
Differential traffic analysis
Aim: to intercept and analyse data sent by apps to destinations on the internet
Data sources: 24 apps downloaded to a Google Pixel 1 running Android 7.1
Tools: Agrigento framework (https://github.com/ucsb-seclab/agrigento), a set of programs that allows monitoring of data transmission from app to network without interfering with the app program
Simulation of user interaction by adoption of a dummy user profile and exploration of all features of the app
App run 14 times to establish a baseline of its data sharing behaviour
Alteration of one source of user information, such as device ID or location, and app run for 15th time
Observation for any deviations in network traffic compared with baseline behaviour, defined as a privacy leak
15th run repeated for each of 28 prespecified sources of sensitive user information, altering one source for each run
Privacy leaks inferred when sensitive information was sent to a remote server, outside of the app
Companies receiving sensitive user data identified by their IP addresses using the WHOIS, Shodan, and GeoIP databases
Content and network analysis
Aims: to describe the characteristics of companies receiving sensitive user data and their data sharing relations from a systems perspective
Data sources: Crunchbase profiles, developers’ websites, company social media profiles, news media articles, app privacy policies, and terms and conditions
Tools: author generated data extraction form in RedCap, analysis in R (3.5.2) using tidygraph (1.1.1)
Two investigators, working independently, extracted data into the RedCap form
One investigator collected data before, and one after, implementation of the General Data Protection Rules (GDPR)
Collated extracted data, resolved errors, and took more recent information in case of discrepancy
Documented additional data sharing relations found in app privacy policies
Descriptive analysis of company characteristics
Quantitative, descriptive analysis of data sharing among apps and third parties identified in the traffic analysis
Simulation of the potential distribution of user data among apps (presuming one person used all the apps in the sample), third parties identified in the traffic analysis, and “fourth parties” that can integrate with third parties
Calculated the number of data sources an entity could access directly from an app, or indirectly through a data sharing partnership with an intermediary
We purposefully sampled medicines related apps that were considered prominent owing to being highly downloaded, rated in the top 100, or endorsed by credible organisations. During 17 October to 17 November 2017, we triangulated two sampling strategies to identify apps. In the first strategy we used a crawling program that interacted directly with the app store’s application programming interface. This program systematically sampled the metadata for the top 100 ranked free and paid apps from the Medical store category of the United Kingdom, United States, Australian, and Canadian Google Play stores on a weekly basis. In the second strategy we screened for recommended or endorsed apps on the website of an Australian medicines related not-for-profit organisation, a curated health app library, a published systematic review, and personal networks of practising pharmacists.
One investigator screened 821 apps for any app names that were potentially related to medicines (ie, managing drugs, adherence, medicines or prescribing information) and excluded apps with irrelevant names (eg, “Pregnancy Calendar,” “Gray’s Anatomy–Atlas,” “Easy stop Smoking,” “Breathing Zone”) (fig 1). Two investigators then independently screened 67 app store descriptions according to the following inclusion criteria:
● Pertains to medicines, such as managing drugs, adherence, medicines or prescribing information
● Available for the Android mobile platform in Google Play to an Australian consumer
● Requests at least one “dangerous” permission, as defined by Google Play,15 or claims to collect or share user data
● Has some degree of interactivity with the user, defined as requiring user input.
We excluded apps if they were available exclusively to customers of a single company (pharmacy, insurance plan, or electronic health record), were targeted at or restricted to use in a single country (ie, a formulary app for UK health professionals employed by the National Health Service), were prohibitively expensive (>$100; >£76; >€88), or were no longer available during the analysis period.
The methods of the traffic analysis are described in detail elsewhere.16 For this analysis we made use of Agrigento, a tool for detecting obfuscated privacy leaks such as encoding or encryption in Android apps. In a laboratory setting, between November and December 2017, we downloaded each app onto a Google Pixel 1 smartphone running Android 7.1. We purchased subscriptions when required (in the form of in-app purchases).
Between December 2017 and January 2018 we simulated real world, in-depth use of the app using four dummy scripted user profiles (one doctor, one pharmacist, and two consumers; see supplementary file), including logging in and interacting with the app while it was running, which involved manually clicking on all buttons, adjusting all settings, and inputting information from the dummy profile when applicable. As all apps were available to the public, we randomised the dummy user profiles irrespective of the app’s target user group.
Using one randomly assigned dummy scripted user profile for each app, we ran the app 14 times to observe its “normal” network traffic related to 28 different prespecified types of user data, such as Android ID, birthday, email, precise location, or time zone. Fourteen executions of the app were required to establish a baseline and to minimise the occurrence of false positives.16 Then we modified one aspect of the user’s profile (eg, location) and ran the app a 15th time to evaluate any change in the network traffic. This differential analysis allowed the detection of an incidence of user data sharing by observing any deviations in network traffic. Change in traffic during the 15th run indicated that the modified aspect of the user’s profile was communicated by the app to the external network, meaning that user data were shared with a third party. We repeated the 15th run for each of the 28 prespecified types of user information, altering one type of data for each run.
The results of the traffic analysis included a list of domain names and respective IP addresses receiving user data and the specific types of user data they received. We identified the recipients of user data by integrating Agrigento with Shodan, a search engine for servers, to obtain geographical information for IP addresses. To reveal the identity of the entities involved, we used the public WHOIS service, a database of domain registrations. Leveraging these tools, we were able to obtain information about the hosts that receive data from the apps, such as location and owner of the remote server.
For each of the entities receiving user data in the traffic analysis, two investigators independently examined their Crunchbase profile, company website, and linked documents such as privacy policies, terms and conditions, or investor prospectus. The investigators extracted data related to the company’s mission, main activities, data sharing partnerships, and privacy practices related to user data into an open ended form in RedCap.17 Data were extracted between 1 February 2018 and 15 July 2018; one investigator extracted data before, and the other after, the General Data Protection Rules (GDPR) were implemented in the European Union in May 2018, which meant that some developers disclosed additional data sharing partnerships in their privacy policies.18 Any discrepancies were resolved through consensus or consolidation and by taking the more recent information as accurate.
We classified entities receiving user data into three categories: first parties, when the app transmitted user data to the developer or parent company (users are considered second parties); third parties, when the app directly transmitted user data to external entities; and fourth parties, companies with which third parties reported the ability to further share user data. We calculated descriptive statistics in Excel 2016 (Microsoft) for all app and company characteristics. Using NVivo 11 (QSR International), we coded unstructured data inductively, and iteratively categorised each company based on its main activities and self reported business models.
The second network represents the potential sharing of user data within the mobile ecosystem, including to fourth parties. To simplify the representation, we grouped apps, their developers, and parent companies into “families” based on shared ownership, and we removed ties to third parties that only provided infrastructure services as they did not report further data sharing partnerships with fourth parties. We report third and fourth parties’ direct and indirect access to app users’ data and summarise the scope of data potentially available to third and fourth parties through direct and indirect channels. This simulation assumes that the same person uses all apps in our sample and it shows how her or his data get distributed and multiplied across the network, identifying the most active distributors of data and the companies that occupy favourable positions in the network, enabling each to gather and aggregate user data from multiple sources.
Patient and public involvement
We undertook this research from the perspective of an Australian app user and in partnership with the Australian Communications Consumer Action Network (ACCAN), the peak body for consumer representation in the telecommunications sector. In continuation of an existing partnership,21 we jointly applied for funding from the Sydney Policy Lab, a competition designed to support and deepen policy partnerships. A representative from ACCAN was involved in preparing the funding application; designing the study protocol, including identifying outcomes of interest; team meetings related to data collection and analysis; preparing dissemination materials targeted at consumers; and designing a dissemination strategy to consumers and regulators.
Overall, 24 apps were included in the study (table 1). Although most (20/24, 83%) appeared free to download, 30% (6/20) of the “free” apps” offered in-app purchases and 30% (6/20) contained advertising as identified in the Google Play store. Of the for-profit companies (n=19), 13 had a Crunchbase profile (68%).
Data sharing practices
As per developer self report in the Google Play store, apps requested on average 4 (range 0-10) “dangerous” permissions—that is, data or resources that involve the user’s private information or stored data or can affect the operation of other apps.15 Most commonly, apps requested permission to read or write to the device’s storage (19/24, 79%), view wi-fi connections (11/24, 46%), read the list of accounts on the device (7/24, 29%), read phone status and identity, including the phone number of the device, current cellular network information, and when the user is engaged in a call (7/24, 29%), and access approximate (6/24, 25%) or precise location (6/24, 25%).
In our traffic analysis, most apps transmitted user data outside of the app (17/24, 71%). Of the 28 different types of prespecified user data, apps most commonly shared a user’s device name, operating system version, browsing behaviour, and email address (table 2). Out of 104 detected transmissions, aggregated by type of user data for each app, 98 (94%) were encrypted and six (6%) occurred in clear text. Out of 24 sampled apps, three (13%) leaked at least one type of user data in clear text, whereas the remainder 14 (58%) only transmitted encrypted user data (over HTTPS) or did not transmit user data in the traffic analysis (7/24, 29%). After implementation of the GDPR, developers disclosed additional data sharing relations within privacy policies, including for two additional apps that had not transmitted any user data during the traffic analysis. Thus, a total of 19/24 (79%) sampled apps shared user data (see supplementary table 2).
Table 3 displays the data sharing practices of the apps (see supplementary table 2 for overview of data sharing practices) detected in the traffic analysis and screening of privacy policies. We categorised first and third parties receiving user data as infrastructure providers or analysis providers. Infrastructure related entities provided services such as cloud computing, networks, servers, internet, and data storage. Analysis entities provided services related to the collection, collation, analysis, and commercialisation of user data in some capacity.
Recipients of user data
Developers engaged a range of third parties who directly received user data and provided services, ranging from error reporting to in-app advertising to processing customer service tickets. Most of these services were provided on a “freemium” basis, meaning that basic services are free to developers, but that higher levels of use or additional features are charged.
We categorised 18 entities (18/55, 33%) as infrastructure providers, which included cloud services (Amazon Web Services, Microsoft Azure), content delivery networks (Amazon CloudFront, CloudFlare), managed cloud providers (Bulletproof, Rackspace, Tier 3), database platforms (MongoDB Cloud Services), and data storage centres (Google). Developers relied on the services of infrastructure related third parties to securely store or process user data, thus the risks to privacy are lower. However, sharing with infrastructure related third parties represents additional attack surfaces in terms of cybersecurity. Several companies providing cloud services also offered a full suite of services to developers that included data analytics or app optimisation, which would involve accessing, aggregating, and analysing app user data. The privacy policies of these entities, however, stated this would occur within the context of a relationship with the developer-as-client and thus likely does not involve commercialising app user data for third party purposes.
We categorised 37 entities (37/55, 67%) as analysis providers, which involved the collection, collation, analysis, and commercialisation of user data in some capacity. Table 4 characterises these analysis providers based on their main business activities.
A systems view of privacy
While certain data sources are clearly sensitive, personal, or identifying (eg, date of birth, drug list), others may seem irrelevant from a privacy perspective (eg, device name, Android ID). When combined, however, such information can be used to uniquely identify a user, even if not by name. Thus, we conducted a network analysis to understand how user data might be aggregated. We grouped the 55 entities identified in the traffic analysis into 46 “families” based on shared ownership, presuming that data as an asset was shared among acquiring, subsidiary, and affiliated companies as was explicitly stated in most privacy policies.23 For example, the family “Alphabet,” named for the parent company, is comprised of Google.com, Google Analytics, Crashlytics, and AdMob by Google.
Third party sharing
Supplementary figure 1 displays the results of the network analysis containing apps, and families of first and third parties that receive user data and are owned by the same parent company. The size of the entity indicates the volume of user data it sends or receives. We differentiated among apps (orange), companies whose main purpose in receiving data was for analysis, including tracking, advertising, or other analytics (grey), and companies whose main purpose in receiving data was infrastructure related, including data storage, content delivery networks, and cloud services (blue).
From the sampled apps, first and third parties received a median of 3 (interquartile range 1-6, range 1-24) unique transmissions of user data, defined as sharing of a unique type of data (eg, Android ID, birthdate, location) with a first or third party. Amazon.com and Alphabet (the parent company of Google) received the highest volume of user data (both received n=24), followed by Microsoft (n=14). First and third parties received a median of 3 (interquartile range 1-5; range 1-18) different types of user data from the sampled apps. Amazon.com and Microsoft, two cloud service providers, received the greatest variety of user data (18 and 14 types, respectively), followed by the app developers Talking Medicines (n=10), Ada Health (n=9), and MedAdvisor International (n=8).
Fourth party sharing
Supplementary figure 2 displays the results of a network analysis conducted to understand the hypothetical data sharing that might occur within the mobile ecosystem at the discretion of app developers, owners, or third parties. Analysis of the websites and privacy policies of third parties revealed additional possibilities for sharing app users’ data, described as “integrations” or monetisation practices related to data (eg, Facebook disclosed sharing end user data with data brokers for targeted advertising). Integrations allowed developers to access and export data through linked accounts (eg, linking a third party analytics and advertising service); however, privacy policies typically stipulated that once data were sent to the integration partner, the data were subject to the partner’s terms and conditions.
Supplementary figure 2 displays the network including fourth parties. All the companies in the fourth party network receive user data for the purposes of analysis, including user behaviour analytics, error tracking, and advertising. We classified entities in the fourth party network by sector, based on their keywords in Crunchbase, to understand how health related app data might travel and to what end.
The fourth party network included 237 entities including 17 app families (apps, developers, and their parent companies in orange) (17/237, 7%), 18 third parties (18/237, 8%), and 216 fourth parties (216/237, 91%); 14 third parties were also identified as fourth parties (14/237, 6%) meaning that these third parties identified in the traffic analysis could also receive data from other third parties identified in the traffic analysis. Supplementary figure 2 shows that most third and fourth parties in the network (blue) could be broadly characterised as software and technology companies (120/220, 55%), whereas 33% (72/220) were explicitly digital advertising companies (grey), 8% (17/220) were owned by private equity and venture capital firms (yellow), 7 (3%) were major telecommunications corporations (dark grey), and 1 (1%) was a consumer credit reporting agency (purple). Only three entities could be characterised predominantly as belonging to the health sector (1%) (brown). Entities in the fourth party network potentially had access to a median of 3 (interquartile range 1-11, range 1-140) unique transmissions of user data from the sampled apps.
Our analysis of the data sharing practices of top rated medicines related apps suggests that sharing of user data is routine, yet far from transparent. Many types of user data are unique and identifying, or potentially identifiable when aggregated. A few apps shared sensitive data such as a user’s drug list and location that could potentially be transmitted among a mobile ecosystem of companies seeking to commercialise these data.
Strengths and limitations of this study
This traffic analysis was conducted at a single time point, performed on a small sample of popular apps, and is limited in terms of scalability. Thus the apps analysed might no longer be available, could have been updated, or might have changed their data sharing practices. We purposefully sampled apps to include widely downloaded ones that were likely to collect and share user data (ie, requested “dangerous” permissions and had some degree of user interactivity). It is not, however, known how the data sharing practices of these apps compare with those of mobile health apps in general. A strength of this approach was in-depth use of the app using simulated user input, including logging in and interacting with the app while it was running. The use of the Agrigento tool allowed detection of privacy leaks that were obfuscated by encoding or encryption, for example.16 This sample is not representative of medicines related apps as a population; however, this approach benefited from focusing on the medicines related apps likely to be used by clinicians and consumers. Because all apps were available to the public and many had multiple functionalities and target users, we could not clearly classify apps as targeted at consumers or health professionals and randomised the simulated user profiles irrespective of target user group. Thus, it is not known whether or how patterns in user data collection and sharing differ among target user groups, which is an important question for future research. Our analysis was restricted to Android apps, thus it is not known whether the iOS versions of these apps or medicines related apps developed exclusively for iPhone differ in data sharing practices. Future work might explore the role of Alphabet (the parent company of Google) within a data sharing network of iOS apps to see whether its dominance is associated with the type of operating system. Our characterisation of the main activities and data sharing relations of entities is based on developers’ self reported practices at the time of analysis and represents our interpretation of these materials. Data were, however, extracted in duplicate and discussed to ensure interpretation was robust.
Comparison with other studies
Our findings are consistent with recent large scale, crowd sourced analyses of app sharing of user data. An analysis of 959 426 apps in the Google Play store found a median of five third party trackers were embedded in each app’s source code and that these were linked to a small number of dominant parent companies, such as Alphabet.4 Analyses of data collected using the Lumen app found that 60% of 1732 monitored apps shared user data with at least one domain associated with advertising or tracking, or both, and 20% shared with at least five different services.3 The top domains were Crashlytics, a Google owned error reporting service that also provides app testing and user analytics, and Facebook Graph API that allows app users to connect with their Facebook account, but also provides analytic services and cross platform advertisement delivery.3 A second analysis of the Lumen app dataset identified 2121 advertising or tracking services, or both receiving user data from 14 599 apps on 11 000 users and characterised these according to their parent organisations.23 Despite owning just 4% of all third party tracking services identified, Alphabet had a presence in more than 73% of apps in the dataset; Facebook and Verizon Communications were similarly identified as having achieved monopoly positions within the mobile ecosystem.23
Conclusions and policy implications
The collection and commercialisation of app users’ data continues to be a legitimate business practice. The lack of transparency, inadequate efforts to secure users’ consent, and dominance of companies who use these data for the purposes of marketing, suggests that this practice is not for the benefit of the consumer.10 Furthermore, the presence of trackers for advertising and analytics, uses additional data and processing time and could increase the app’s vulnerability to security breaches.25 In their defence, developers often claim that no “personally identifiable” information is collected or shared. However, the network positions of several companies who control the infrastructure in which apps are developed, as well as the data analytics and advertising services, means that users can be easily and uniquely identified, if not by name. For example, the semi-persistent Android ID will uniquely identify a user within the Google universe, which has considerable scope and ability to aggregate highly diverse information about the user. Taking a systems view of the mobile ecosystem suggests that privacy regulation should emphasise the accountabilities of third parties, known as “data processors,” in addition to first parties or “data controllers.”18 Currently, within the “big data” industry, users do not own or control their personal data1011; at minimum, regulators should insist on full transparency, requiring sharing as opposed to privacy policies. The implementation of the GDPR in the European Union resulted in greater transparency around data sharing relationships among some developers in our sample. However, as big data features increasingly in all aspects of our lives, privacy will become an important social determinant of health, and regulators should reconsider whether sharing user data for purposes unrelated to the use of a health app, for example, is indeed a legitimate business practice. At minimum, users should be able to choose precisely which types of data can be accessed and used by apps (eg, email, location), and to have the option to opt-out for each type of data. More effective regulation, however, might focus instead on third parties engaged in commercialising user data or the companies that own and operate the smartphone platforms and app stores.4
Clinicians should be conscious about the choices they make in relation to their app use and, when recommending apps to consumers, explain the potential for loss of personal privacy as part of informed consent. Privacy regulators should consider that loss of privacy is not a fair cost for the use of digital health services.
What is already known on this topic
Developers of mobile applications (apps) routinely, and legally, share user data
Most health apps fail to provide privacy assurances or transparency around data sharing practices
User data collected from apps providing medicines information or support may be particularly attractive to cybercriminals or commercial data brokers
What this study adds
Medicines related apps, which collect sensitive and personal health data, share user data within the mobile ecosystem in much the same way as other types of apps
A small number of companies have the potential to aggregate and perhaps re-identify user data owing to their network position
We thank Chris Klochek for developing the app store crawling program and Tanya Karliychuk, grants officer at the Australian Communications Consumer Action Network, for advising on the study design, analysis, and dissemination strategy.
Contributors: QG acquired funding, designed the study, supervised and participated in data collection and content analysis, and wrote the first draft of the manuscript. KC participated in data collection and content analysis and critically revised manuscript drafts. FH participated in designing the study, conducted the network analysis, and critically revised manuscript drafts. AC conducted the traffic analysis and critically revised manuscript drafts. LB participated in designing the study and commented on the draft. RH designed the study, supervised the traffic analysis, and critically revised manuscript drafts. QG attests that all listed authors meet authorship criteria and that no others meeting the criteria have been omitted. QG and RH act as guarantors.
Funding: This work was funded by a grant from the Sydney Policy Lab at The University of Sydney. QG was supported by a postdoctoral fellowship from the Canadian Institutes of Health Research. The Sydney Policy Lab had no role in the study design; in the collection, analysis, and interpretation of data; in the writing of the report; or in the decision to submit the article for publication.
Competing interests: All authors have completed the ICMJE uniform disclosure form at www.icmje.org/coi_disclosure.pdf and declare: this work was funded by the Sydney Policy Lab; no financial relationships with any organisations that might have an interest in the submitted work in the previous three years; no other relationships or activities that could appear to have influenced the submitted work.
Ethical approval: Not required.
Data sharing: The full analysis is publicly available at: https://healthprivacy.info/.
Transparency: The lead author (QG) affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and that any discrepancies from the study as planned have been explained.
This is an Open Access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/.