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The ability to collate data of healthcare clients from disparate health information systems in the public health system in the Western Cape province of South Africa presents opportunities for improved healthcare outcomes, but it also necessitates the development of systems to address risks related to the potential for data breach and privacy violations. Here, we describe the data governance infrastructure for the Provincial Health Data Centre (PHDC) in the Western Cape Province of South Africa, which consolidates person-level health data from various sources to support healthcare management and research. We describe how PHDC data governance protects sensitive health data by ensuring compliance with privacy regulations and legislation, and demonstrate the adoption of key pillars of data governance in order to implement policies and procedures that safeguard patient privacy and promote responsible data use. We review downstream impacts of data collation including improved continuity of care, outbreak detection and research support, and how these may be facilitated within the data governance infrastructure. The presentation highlights the need to balance the potential benefits of routine health data collation and secondary use with managing the risks to privacy and security for healthcare clients, and describes how this is addressed by the PHDC. Future areas to be considered include adoption of Trusted Research Environments (TRE) and the use of synthetic data, as well as identifying effective ways of enforcing data governance policies and monitoring compliance levels.

Introduction The Patient Master Index (PMI) plays an important role in management of patient information and epidemiological research, and the availability of unique patient identifiers improves the accuracy when linking patient records across disparate datasets. In our environment, however, a unique identifier is seldom present in all datasets containing patient information. Quasi identifiers are used to attempt to link patient records but sometimes present higher risk of over-linking. Data quality and completeness thus affect the ability to make correct linkages. Aim This paper describes the record linkage system that is currently implemented at the Provincial Health Data Centre (PHDC) in the Western Cape, South Africa, and assesses its output to date. Methods We apply a stepwise deterministic record linkage approach to link patient data that are routinely collected from health information systems in the Western Cape province of South Africa. Variables used in the linkage process include South African National Identity number (RSA ID), date of birth, year of birth, month of birth, day of birth, residential address and contact information. Descriptive analyses are used to estimate the level and extent of duplication in the provincial PMI. Results The percentage of duplicates in the provincial PMI lies between 10% and 20%. Duplicates mainly arise from spelling errors, and surname and first names carry most of the errors, with the first names and surname being different for the same individual in approximately 22% of duplicates. The RSA ID is the variable mostly affected by poor completeness with less than 30% of the records having an RSA ID. The current linkage algorithm requires refinement as it makes use of algorithms that have been developed and validated on anglicised names which might not work well for local names. Linkage is also affected by data quality-related issues that are associated with the routine nature of the data which often make it difficult to validate and enforce integrity at the point of data capture.

Objectives The UK Longitudinal Linkage Collaboration (UKLLC) was established to link and integrate data from many longitudinal population studies (LPS) with participants’ records from diverse governmental data owners across the UK. The UKLLC aims to unify disparate requirements from many data owners in different legal jurisdictions into one, predictable, access framework. Approach The UKLLC framework provides a legal and governance ‘funnel’ which enables diverse data owner requirements to be captured; for UKLLC to centrally control these; and - from a research applicants perspective - there to be a predictable single process that is bound into emerging national standards for data science (e.g., the ‘Five Safes’ framework). This is applied through a Trusted Research Environment (TRE) approach where ISO27001 and Digital Economy Act accredited contracts, data sharing agreements, policies and procedures define and permit data flows and provide organisational separation of key functions including the third-party processing of participant identifiers for linkages. Results We developed a project review mechanism enabling UKLLC to efficiently process applications whilst respecting core data owners requirements: each aspect of this is tailored to relevant legal, jurisdictional and accreditation requirements. Contracting specialist processors enabled UKLLC to implement ‘organisational separation’ between use of participant identifiers and de-personalised data, enabling a ‘functionally anonymous’ approach facilitating efficient onward sharing of integrating data. The processes we have developed promote transparency and trust with regulators, participants and public. UKLLC host (University of Bristol) is able to be the data controller of data within the UKLLC; make application decisions; create the Licenced Dataset; and, curate and reuse data and metadata products of the research whilst data is maintained functionally anonymous. Large-scale complex linked data-sets are provisioned in a timely manner. Conclusion The UKLLC is part of a large ecosystem of organisations. A delegated and distributed model has been established supported by our contracts and policy. This configuration has allowed the delivery of cross-cutting data-sets to address priority Covid-19 research questions and a generalisable and sustainable model for the future

Objectives To date South Africa has experienced four distinct COVID-19 waves due to ancestral, Beta, Delta and Omnicron SARS-CoV-2 variants. We sought to answer pertinent public health questions in a timely manner as new COVID-19 variants emerge(d) using routine health service data linked through a service-facing health information exchange (HIE). Approach A population cohort was defined amongst regular health service users in the Western Cape Province of South Africa based on recent utilisation of public sector services as reflected in the Provincial Health Data Centre (PHDC) which functions as a HIE. Infection, hospitalisation and mortality data were derived from routinely linked laboratory, service and national vital registration data sources. Serology done on residual specimens of patients monitored for HIV and diabetes treatment progress were linked to the PHDC, as were vaccination data from the national vaccination information system. A single linked and de-identified dataset was exported for analysis purposes. Results Based on accessing services in the preceding 3 years, a cohort of 3.5 million adult patients could be enumerated and linked to co-morbidity and SARS-CoV-2 outcome data. Serology from 16,000 specimens spread across the three inter-wave periods, and vaccine data from amongst the 5 million vaccine doses given in the Province, could also be linked. Variants could be identified by wave or by PCR assay target anomalies during cross-over periods. Publishable variant severity analyses were feasible from the sub-cohort of patients with diagnosed COVID-19, and variant-specific vaccine effectiveness was assessible amongst cases, in the population cohort, and in patients with HIV. The impact of prior infection and marginal value of vaccination in those with prior infection was assessible within the serology sub-cohort. Conclusion A single linked de-identified dataset derived from an operational HIE was able to quickly address critical public health questions related to COVID-19 variants in a privacy-preserving manner.

Objectives To describe the record linkage system that is currently implemented at the Provincial Health Data Centre (PHDC) in the Western Cape, South Africa To assess its output to date with respect to types of matches and duplicates trends To describe the errors affecting patient matching Approach We apply a stepwise deterministic record linkage approach to link patient data that are routinely collected from health information systems in the Western Cape province of South Africa. Variables used in the linkage process include South African National Identity number (RSA ID), date of birth, year of birth, month of birth, day of birth, residential address and contact information. Matching records are established from sequentially running the data through multiple passes formed by various combinations of linkage variables. Descriptive analyses are used to estimate the extent of mismatches and duplication in the provincial patient master index (PMI). Results The proportion of duplicates dropped from approximately 16.8% in December 2015 to 9.6% in October 2020, indicating improved data linkage over time. Duplicates mainly arise from spelling errors, and surname and first names carry most of the errors, with different first names and surname for the same individual in approximately 22% of duplicates. Linkage is also affected by completeness, with less than 30 % completeness for the South African national identity (RSA ID) number which is mainly because RSA ID is not mandatory when seeking healthcare. Conclusion Linkage improvement could be due to improved registration practices. Further improvements are possible by repeating data linkage where patients register before creating a new patient record following a failed search. This could use the PHDC linkage approach whilst leveraging all data in addition to search terms used by the clerk.

Objectives In the Western Cape, South Africa, public-sector individual-level routine data are consolidated from multiple non-EMR sources through the Provincial Health Data Centre (PHDC). This enables the description of temporal changes in population-wide antenatal HIV sero-prevalence. We evaluated the validity of these data compared to aggregated program data and sentinel surveys. Approach We conducted a retrospective cohort analysis of all pregnancies consolidated in the PHDC from January 2011 to December 2020. Evidence of antenatal and HIV care from electronic platforms (each with its own multi-source phenotype algorithm) were linked using a unique patient identifier. HIV prevalence estimates were triangulated with available sentinel national and provincial antenatal survey estimates as well as aggregated programmatic data from registers as recorded in the District Health Information System. Provincial, district-level and age-group HIV prevalence estimates were compared between data systems using correlation coefficients, absolute differences and trend analysis. Results In total, 982,914 pregnancies were ascertained with a median maternal age of 26.9 years. Between 2011–2013, PHDC HIV prevalence estimates were widely disparate from aggregate and survey data (due to incomplete electronic data) whereas, from 2014 onwards, estimates were more closely correlated to aggregate data estimates (r=0.83; p=0.02) with an average absolute prevalence difference of 0.97%. In keeping with survey and aggregate data trends, PHDC data show a relatively stable provincial HIV prevalence from 17.0 (95%CI 16.8%–17.2%) in 2015 to 18.9% (95%CI 18.7–19.1%) in 2020. The highest HIV prevalence was in the Cape Metro district (20.6%; 95%CI 20.4%–20.9%). Prevalence estimates by age group were comparable between sentinel surveys and PHDC from 2014 onwards with an average absolute prevalence difference of 1.6%. Conclusion This study is the first to compare sentinel sero-prevalence surveys with both register-based aggregate data and consolidated individuated administrative data. We show that in this setting linked individuated data may be reliably used for HIV surveillance and provide more granular estimates with greater efficiency.

Introduction There are few examples of functional health information exchange environments or data linkage centres in the African settings. New opportunities are emerging as unique health identifiers and patient registration systems are being established nationally in many countries, and increasingly individuated health data are available for linkage, often linked to priority global health initiatives such as to support HIV and tuberculosis services. Objectives and Approach We sought to establish a province-wide health information exchange and data centre for individuated health data, leveraging a unique identifier and available individuated data. The intention with the Provincial Health Data Centre (PHDC) was to create a single well-governed environment which could simultaneous fulfil the functions of an exchange directly supporting care, as well as support research requests. Often the demands of academics, funders and global agencies for reporting result in data consolidation for research and reporting taking precedence over service delivery. Results Through pragmatic use of data from all sources, the PHDC is able to usefully enumerate many health conditions of interest with sufficient fidelity for both service and research purposes. For research data requests, there has been a huge improvement in data governance alongside increased data availability as a result of the single environment with clear procedures for patient protection, and the benefits of data linkage prior to anonymisation. Many of the inference approaches have benefited hugely from interactions with researchers, which has in turn improved the quality of outputs for routine care. Conclusion / Implications In maturing digital health environments which are establishing consolidated data environments for the first time, aspiring from the start to a single well-governed environment for both patient care and research, is a virtuous model with many benefits over fragmented data linkage efforts

Introduction Increasing use of digital medical records creates disparate data resources for the same health care client population; and harnessing the benefits of real-time health data requires effective data linkage. A South African Health Information Exchange (HIE) collates and links routine health data from multiple sources, running daily updates through an automated ETL process. Many existing deterministic and probabilistic algorithms link person-level data using demographic identifiers, and can be combined in an optimised methodological pipeline. The performance of such pipelines must be validated against known matched pairs. The HIE uses current algorithms for record linkage, but methods that rely on similar spelling, name frequency and phonetic matching have been optimised for non-African names, and are not as effective. Objectives We assessed common problems arising in the linkage process in the HIE, using this information to compile a curated representative African validation database for optimising existing and new linkage pipelines. Results Using current linkage algorithms, we have identified the proportion of duplicates in the last five years, ranging from 25% in 2015 and stabilising at 10% by 2019. Common causes of duplicates across the whole database include mismatch in first name (37%), surname (17%), date of birth (13%), sex (8%) and South African Identification Number (0.2%). Complications from new-born naming and records of twins affect >8% of all records, and temporary health identifiers assigned at birth, during emergency response, and during poor connectivity of facilities to the provincial patient master index affect 2% of records. Conclusions Based on these data, we have constructed a South African-specific, representative validation dataset that contains linkage pairs that represent placeholder phrases for newborns prior to naming (e.g. “baby of”), language variations; twins; character insertions, substitution and omissions in names with similar spellings; frequencies of names in the general population; and similar-sounding names.

Introduction In the Western Cape Province of South Africa, a wealth of patient-level data is collected through many separate electronic systems, which share a unique health identifier. However, clinicians primarily access paper folders, which can be unreliable, difficult to locate and are at risk of loss. Patients frequently attend multiple facilities and their information may not be accessible across facilities, hampering continuity of care. Objectives and Approach Facilitated by the unique health identifier, a provincial Health Information Exchange (HIE), harmonises patient level data from routine systems into a health information exchange daily. The Single Patient Viewer (SPV) has been developed as a prototype web-based electronic health record and data access portal. SPV integrates clinical data for a single patient both longitudinally and cross-sectionally, in tabular and graphical views, to assist clinicians in rapid information discovery. The application is designed as a web application that calls a multi-purpose API that facilitates interoperability with the HIE. Result SPV is in an extended pilot phase with over 200 clinicians using the portal, either for clinical care provision, or for clinical audit activities. The application has evolved to include referral, follow-up (voice call and messaging) and reporting functionality. In the past 6 months, over 17,000 patients have been viewed with an average daily search of 150 patients. An anonymised user survey with 52 participants showed that users felt that SPV was enjoyable to use, easy to learn, innovative, and supportive and valuable to their work. Conclusion / Implications SPV has been developed as a global public good project and will be made freely available once matured. A unique feature of the development of SPV is that clinicians were using it while it was being built, enabling constant clinical user feedback. SPV demonstrates the value of integrating health data for clinical viewing while interoperable systems mature.

Introduction Electronic tuberculosis (TB) register systems influence policy decisions, resource allocation and patient care in many ways, but their limitations have been demonstrated in many high-burden settings like South Africa. While digital health systems in the Western Cape, South Africa have improved over time and benefited from implementation of a unique patient identifier, questions about quality and completeness of register data remain. A Health Information Exchange (HIE), established in 2015, daily integrates routinely-collected person level health data from electronic sources in the Province, including laboratory, dispensing, clinical and encounter data, as well as disease register data for HIV and TB. Objectives and Approach Using TB-related datapoints from various electronic platforms and resources, an algorithm was developed to infer cases, visit and treatment information, comorbidities and mortality - defined as a “cascade”. The cascade is recompiled daily incorporating new information added to the HIE, and presented to health care workers and managers as filterable, downloadable reports on an electronic platform. TB Register and inferred cascade data were compared for 2018. Results There were 40,227 cases in the register after 3,010 duplicate entries were eliminated by consolidating personal identifiers and duplicate entries across facilities into single TB episodes. 13,729 additional cases were identified in the HIE cascade. Of these, 6,984 had evidence of treatment; 4,143 were diagnosed and treated only in hospitals - thus less likely to be recorded in the registers. Updated patient contact details and allocation of a primary care facility based on patient visit history, aided in patient care. Conclusion / Implications Leveraging a consolidated environment for person-level health data can substantially enhance and verify disease registers. Appropriate tools can render these data accessible and actionable to improve patient care, minimise errors and missed opportunities to close treatment gaps, and increase accuracy of surveillance and reporting on a programmatic level.

Introduction The Western Cape Provincial Health Data Centre (PHDC) consolidates person-level clinical data across government services, leveraging sustained investments in patient registration systems, a unique identifier, and maturation of administrative and clinical digital health systems. Objectives The PHDC supports clinical care directly through tools for clinicians which integrate patient data or identify patients in need of interventions, and indirectly through supporting operational and epidemiological analyses. Methods The PHDC is housed entirely within government. Data are processed from a range of source systems, usually daily, through distinct harmonisation and curation, beneficiation, and reporting processes. Linkage is predominantly through the unique identifier which doubles as a pervasive folder number, augmented by other identifiers. Further data processing includes triangulation of multiple data sources for enumerating health conditions, with assignment of certainty levels for each enumeration. Outputs include patient-specific email alerts, a web-based consolidated patient clinical viewing platform, filterable line-listings of patients with specific conditions and associated characteristics and outcomes, management reports and dashboards, and data releases in response to operational and research data requests. Strict architectural, administrative and governance processes ensure privacy-protection. Results In the past decade 8 million unique people are recorded as having sought healthcare in the provincial public sector health services, with current utilisation at 15 million attendances or admissions a year. Cross-sectional enumeration of health conditions includes over 430 000 people with HIV, 500 000 with hypertension, 235 000 with diabetes. 110 000 pregnancies and 54 000 patients with tuberculosis are enumerated annually. Each year over 50 data requests are processed for internal and external requesters in accordance with data request and release governance processes. Conclusions The single consolidated environment for person-level health data in the Western Cape has created new opportunities for supporting patient care, while improving the governance around access to and release of sensitive patient data.

Information is increasingly digital, creating opportunities to respond to pressing issues about human populations in near real time using linked datasets that are large, complex, and diverse. The potential social and individual benefits that can come from data-intensive science are large, but raise challenges of balancing individual privacy and the public good, building appropriate socio-technical systems to support data-intensive science, and determining whether defining a new field of inquiry might help move those collective interests and activities forward. A combination of expert engagement, literature review, and iterative conversations led to our conclusion that defining the field of Population Data Science (challenge 3) will help address the other two challenges as well. We define Population Data Science succinctly as the science of data about people and note that it is related to but distinct from the fields of data science and informatics. A broader definition names four characteristics of: data use for positive impact on citizens and society; bringing together and analyzing data from multiple sources; finding population-level insights; and developing safe, privacy-sensitive and ethical infrastructure to support research. One implication of these characteristics is that few people possess all of the requisite knowledge and skills of Population Data Science, so this is by nature a multi-disciplinary field. Other implications include the need to advance various aspects of science, such as data linkage technology, various forms of analytics, and methods of public engagement. These implications are the beginnings of a research agenda for Population Data Science, which if approached as a collective field, can catalyze significant advances in our understanding of trends in society, health, and human behavior.

ABSTRACT Background Sub-Saharan Africa is the region most heavily affected by the HIV/AIDS epidemic. HIV increases the risk of developing cancer but the ascertainment of cancers in patients attending antiretroviral therapy (ART) treatment programs might be incomplete. To estimate the under-ascertainment of cancer we compared incidence rates of AIDS-defining cancers in South African HIV cohorts with and without cancer case ascertainment through record linkage with the National Cancer Registry. Methods We used the data of adult (≥16 years) HIV-positive persons receiving care between 2004 and 2011 at one of four ART programs in South Africa. These programs collaborate with the International Epidemiologic Databases to Evaluate AIDS Southern Africa (www.iedea-sa.org) and collected data for AIDS-defining cancers but not for other cancers. To improve cancer ascertainment we probabilistically linked patient records (using first name, surname, age, and gender) from two HIV cohorts with the cancer records of the South African National Cancer Registry. We calculated incidence rates per 100,000 person-years after starting ART for the AIDS-defining cancers, i.e. Kaposi sarcoma (KS), invasive cervical cancer (ICC) and non-Hodgkin lymphoma (NHL). We compared incidence rates before and after inclusion of record linkage identified cancer cases using the attributable fraction of cancers identified with 95% confidence intervals (CI). Results A total of 49,207 adults starting ART in South Africa were included. 65% of patients were female, median age at starting ART was 35 years (interquartile range 30-41 years). We identified a total of 471 incident cancer cases. With record linkage the incidence increased from 81 to 292 for KS, from 1 to 119 for NHL and 12 to 497 for ICC per 100,000 person-years. The attributable fraction of cancers identified was 72% (95% CI 63-79%) for KS, 98% (95% CI 94-99%) for NHL and 98% (95% CI 95-99%) for ICC. Conclusion Ascertainment of cancer in HIV program data in African settings is incomplete. This case study has shown that probabilistic record linkage to cancer registries is both feasible and essential for cancer ascertainment in HIV cohorts in South Africa.

ABSTRACT Background Electronic administrative data exist in several domains which, if linked, are potentially useful for research. However, benefits from data linkage should be considered alongside risks such as the threat to privacy. Avon Longitudinal Study of Parents and Children (ALSPAC) is a birth cohort study. The Project to Enhance ALSPAC through Record Linkage (PEARL) was established to enrich the ALSPAC resource through linkage between ALSPAC participants and routine sources of health and social data. Qualitative research was incorporated in the PEARL study to examine participants’ views about data linkage and inform approaches to information. This paper focusses on issues of consent. Methods Digitally recorded interviews were conducted with 55 participants aged 17-19 years. Terms and processes relating to consent, anonymization and data linkage were explained to interviewees. Scenarios were used to prompt consideration of linking different sources of data, and whether consent should be requested. Interview recordings were fully transcribed. Thematic analysis was undertaken using the Framework approach. Results Participant views on data linkage appeared to be most influenced by: considerations around the social sensitivity of the research question, and; the possibility of tangible health benefits in the public interest. Some participants appeared unsure about the effectiveness of anonymization, or did not always view effective anonymization as making consent unnecessary. This was related to notions of ownership of personal information and etiquette around asking permission for secondary use. Despite different consent procedures being explained, participants tended to equate consent with ‘opt-in’ consent through which participants are ‘asked’ if their data can be used for a specific study. Participants raising similar concerns came to differing conclusions about whether consent was needed. Views changed when presented with different scenarios, and were sometimes inconsistent. Conclusions Findings from this study question the validity of ‘informed consent’ as a cornerstone of good governance, and the extent to which potential research participants understand different types of consent and what they are consenting, or not consenting, to. Pragmatic, imaginative and flexible approaches are needed if research using data linkage is to successfully realise its potential for public good without undermining public trust in the research process.