Person-level Data Research Articles

Creating a learning health system to include environmental determinants of health: The GroundsWell experience.

Policies aiming to prevent ill health and reduce health inequalities need to consider the full complexity of health systems, including environmental determinants. A learning health system that incorporates environmental factors needs healthcare, social care and non-health data linkage at individual and small-area levels. Our objective was to establish privacy-preserving household record linkage for England to ensure person-level data remain secure and private when linked with data from households or the wider environment. A stakeholder workshop with participants from our regional health board, together with the regional data processor, and the national data provider. The workshop discussed the risks and benefits of household linkages. This group then co-designed actionable dataflows between national and local data controllers and processors. A process was defined whereby the Personal Demographics Service, which includes the addresses of all patients of the National Health Service (NHS) in England, was used to match patients to a home identifier, for the time they are recorded as living at that address. Discussions with NHS England resulted in secure and quality-assured data linkages and a plan to flow these pseudonymised data onwards into regional health boards. Methods were established, including the generation of matching algorithms, transfer processes and information governance approvals. Our collaboration accelerated the development of a new data governance application, facilitating future public health intervention evaluations. These activities have established a secure method for protecting the privacy of NHS patients in England, while allowing linkage of wider environmental data. This enables local health systems to learn from their data and improve health by optimizing non-health factors. Proportionate governance of health and linked non-health data is practical in England for incorporating key environmental factors into a learning health system.

Imbalances in the oral health workforce: a Canadian population-based study

BackgroundIn Canada, a new federal public dental insurance plan, being phased in over 2022–2025, may help enhance financial access to dental services. However, as in many other countries, evidence is limited on the supply and distribution of human resources for oral health (HROH) to meet increasing population needs. This national observational study aimed to quantify occupational, geographical, institutional, and gender imbalances in the Canadian dental workforce to help inform benchmarking of HROH capacity for improving service coverage.MethodsSourcing microdata from the 2021 Canadian population census, we described workforce imbalances for three groups of postsecondary-qualified dental professionals: dentists, dental hygienists and therapists, and dental assistants. To assess geographic maldistribution relative to population, we linked the person-level census data to the geocoded Index of Remoteness for all inhabited communities. To assess gender-based inequities in the dental labour market, we performed Blinder-Oaxaca decompositions for examining differences in professional earnings of women and men.ResultsThe census data tallied 3.4 active dentists aged 25–54 per 10,000 population, supported by an allied workforce of 1.7 dental hygienists/therapists and 1.6 dental assistants for every dentist. All three professional groups were overrepresented in heavily urbanized communities compared with more rural and remote areas. Almost all dental service providers worked in ambulatory care settings, except for male dental assistants. The dentistry workforce was found to have achieved gender parity numerically, but women dentists still earned 21% less on average than men, adjusting for other characteristics. Despite women representing 97% of dental hygienists/therapists, they earned 26% less on average than men, a significant difference that was largely unexplained in the decomposition analysis.ConclusionsAccelerating universal coverage of oral healthcare services is increasingly advocated as an integral, but often neglected, component toward achieving the health-related Sustainable Development Goals. In the Canadian context of universal coverage for medical (but not dentistry) services, the oral health workforce was found to be demarcated by considerable geographic and gendered imbalances. More cross-nationally comparable research is needed to inform innovative approaches for equity-oriented HROH planning and financing, often critically overlooked in public policy for health systems strengthening.

Adjusting the risk-adjustment: Accounting for variation between organisations in the responsiveness of their expenditure to need

There is concern that basing healthcare budgets on risk adjustment estimates derived from historical utilisation data may reinforce patterns of unmet need. We propose a method to avoid this, based on a measure of how closely local health organisations align resources to the needs of their populations. We refer to this measure as the ‘responsiveness of expenditure to need’ and estimate it using national person-level data on use of acute hospital and secondary mental health services in England. We find large variation in responsiveness in both services and show that higher expenditure responsiveness in mental health is associated with fewer suicides. We then re-estimate the national risk-adjustment model removing the data from the organisations with the lowest expenditure responsiveness to need. As expected, higher need individuals are estimated to have higher expenditure needs when less responsive organisations are removed from the estimation of the risk-adjustment. Removal of organisations with below-average responsiveness results in the neediest deciles of individuals having an extra £163 (7%) annual need for acute hospital care and an additional £79 (27%) annual need for mental health services. The application of this approach to risk adjustment would result in more resources being directed towards organisations serving higher-need populations.

Engagement and Communication Challenges for a Complex, Multi-nation Research Project: AD|ARC (Administrative Data | Agricultural Research Collection)

The Administrative Data | Agricultural Research Collection (AD|ARC) is a complex research project involving four nations, a mixture of business-level, household-level and person-level data, and diverse partners, stakeholders and audiences. The project is creating independent but comparable Research Ready Datasets, using linked data for farm businesses, farmers and farm families in the four UK nations. The engagement and communications workstream is crucial to build trust, inform the programme of work, support and enhance scientific objectives, and deliver high-quality, relevant findings that meet stakeholder and funder expectations. The project deployed a bespoke engagement and communications framework to meet these objectives. This presentation shares key elements of AD|ARC project's engagement and communications framework and strategy: Multi-region Approach: Balancing the need for a harmonised, federated resource with respecting regional differences in data, trusted research environments, and stakeholder perspectives. Transparency and Trust: Engaging diverse stakeholders, including farmers, researchers, and policymakers, to build trust, support and legitimacy. Collaborative Decision-Making: Interacting with diverse partners, (data owners, researchers, research subject representatives) to refine research questions. Outreach and Dissemination: A multi-method, iterative approach to raise awareness, share and promote the project. Results are demonstrated by success in being selected as an ADR UK Flagship Dataset; integrating diverse perspectives into the creation of comparable Research Ready Datasets; consensus on a research programme; useful learning which is incorporated into strategy; and actionable evidence to inform policymaking. The presentation will reflect on lessons learned, drawing out implications for research and public engagement that could apply to any complex research project.

Personal characteristics and transmission dynamics associated with SARS-CoV-2 semi-quantitative PCR test results: an observational study from Belgium, 2021-2022.

Following harmonization efforts by the Belgian National Reference Center for SARS-CoV-2, semi-quantitative PCR test (SQ-PCR) results, used as a proxy for viral load, were routinely collected after performing RT-qPCR tests. We investigated both the personal characteristics associated with SQ-PCR results and the transmission dynamics involving these results. We used person-level laboratory test data and contact tracing data collected in Belgium from March 2021 to February 2022. Personal characteristics (age, sex, vaccination, and laboratory-confirmed prior infection) and disease stage by date of symptom onset were analyzed in relation to SQ-PCR results using logistic regression. Vaccine effectiveness (VE) against a high viral load (≥107 copies/mL) was estimated from the adjusted probabilities. Contact tracing involves the mandatory testing of high-risk exposure contacts (HREC) after contact with an index case. Odds ratios for test positivity and high viral load in HREC were calculated based on the SQ-PCR result of the index case using logistic regression models adjusted for age, sex, immunity status (vaccination, laboratory-confirmed prior infection), variant (Alpha, Delta, Omicron), calendar time, and contact tracing covariates. We included 909,157 SQ-PCR results of COVID-19 cases, 379,640 PCR results from index cases, and 72,052 SQ-PCR results of HREC. High viral load was observed more frequently among recent cases, symptomatic cases, cases over 25 years of age, and those not recently vaccinated (>90 days). The vaccine effectiveness (VE) of the primary schedule in the first 30 days after vaccination was estimated at 47.3% (95%CI 40.8-53.2) during the Delta variant period. A high viral load in index cases was associated with an increased test positivity in HREC (OR 2.7, 95%CI 2.62-2.79) and, among those testing positive, an increased likelihood of a high viral load (OR 2.84, 95%CI 2.53-3.19).

Data governance for ethical usage of linked routine health data in South Africa: balancing privacy and data sharing.

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.

The Demographic Frame

The Demographic Frame is a comprehensive, secure, access-controlled database of person-level data consisting of demographic, social, and economic characteristics of individuals derived from census, survey, administrative, and third-party data sources. The Demographic Frame is facilitating innovations in data collection, processing, analyses, and products for census and survey programs.

Linkage and Demographic Indexing for Nursing Dynamics in the Integrated Data Service

Objective and ApproachThis project links UK Nursing and Midwifery Council (NMC) Registrant data to the 2021 England and Wales Census (“Census 21”). The Office for National Statistics (ONS) has composed a framework of index tables to facilitate linkage between prominent datasets in the UK. In linking person-level data to Census 21, ONS’ Demographic Index enables onward linkage to other administrative datasets that are indexed alike. NMC Register and Census 21 records have been initially linked using bespoke deterministic and probabilistic methods, with resulting matches being joined to the Demographic Index. ONS’ Demographic Index Matching Service (DIMS) will also be used to link NMC data to the Demographic Index to enable comparisons between methods. ResultsAnalysis of both approaches will compare quality metrics such as match rate, recall and precision and tools will be used to assess bias in matches made through each method. These metrics will provide an insight into how the index matching services have been designed and whether improvements can be made to account for any shortcomings in their performance. ConclusionsHybrid approaches using both bespoke and indexing methods are proving valuable where sole use of the matching services is not suitable. This research will help ONS to refine which methods will yield the highest quality linkage and when. ImplicationsAs ONS pursues an ‘indexing first’ approach to linkage, the outcomes of this and similar projects will influence the future of utilising indexes in varied capacities when creating or maximising the potential of linked data.

The Border Patrol’s Migrant Death Undercounting in South Texas

Executive Summary For the past 25 years, the Border Patrol has tracked migrant deaths along the US-Mexico border. For nearly the same amount of time, it has also faced criticisms that it failed to capture the true number of migrant deaths in its tally. This article focuses on these undercounting criticisms and asks two questions: (1) How many documented migrant death cases are left out of Border Patrol’s official data? And (2) what factors lead to the Border Patrol’s migrant death undercounting? In particular, the article focuses on three South Texas counties: Brooks County, Kenedy County, and Maverick County. To answer the research questions, this article relies on comparative data analysis. In particular, it compares two person-level datasets: the Border Patrol’s dataset on migrant deaths from 2009 to 2017 and county-level records from the Brooks County Sheriff’s Office, the Kenedy County Sheriff’s Office, and Maverick County Justices of the Peace over the same period. It then attempts to match each county-level record to a recorded death in the Border Patrol’s dataset. Using this process, the article quantifies migrant death undercounting in South Texas, highlights geographic and temporal trends, and tracks the uncounted cases’ specific characteristics. From 2009 to 2017, this comparative data analysis confirmed that the Border Patrol was undercounting migrant deaths across the three South Texas counties. Specifically, the article finds that the Border Patrol failed to include 139 cases, which totaled 19 percent of the counties’ 749 recorded migrant deaths during the study period. This undercounting ranged from 16 percent in Brooks County to 24 percent in Maverick County and 29 percent in Kenedy County, with fluctuating rates over time. The uncounted cases also had specific characteristics. In particular, they were more likely to be skeletal remains, lack an identification, and be discovered by an external entity. These characteristics highlight the various factors behind the Border Patrol’s undercounting, such as issues with the Border Patrol’s migrant death definition, inconsistent data collection from external entities, and the agency’s low prioritization of migrant death tracking. To address and remedy the Border Patrol’s migrant death undercounting requires tackling each underlying factor. First and foremost, this article recommends that the Border Patrol fully train its agents on the agency’s migrant death definition and ensure consistent and standardized outreach to external entities. Further, it recommends that the Border Patrol improve its migrant death count’s accuracy through additional operational changes. These proposed changes include making “accurate migrant death counts” an official objective for the Border Patrol’s Missing Migrant Program, prioritizing a two-way information-sharing process with county-level officials, retroactively including missed migrant deaths in the official count, and publishing more detailed person-level data on migrant deaths.

Enhanced Cohort Methods for HIV Research and Epidemiology (ENCORE): Protocol for a Nationwide Hybrid Cohort for Transgender Women in the United States.

In the United States, transgender women are disproportionately impacted by HIV and prioritized in the national strategy to end the epidemic. Individual, interpersonal, and structural vulnerabilities underlie HIV acquisition among transgender women and fuel syndemic conditions, yet no nationwide cohort monitors their HIV and other health outcomes. Our objective is to develop a nationwide cohort to estimate HIV incidence, identify risk factors, and investigate syndemic conditions co-occurring with HIV vulnerability or acquisition among US transgender women. The study is informed by the Syndemics Framework and the Social Ecological Model, positing that stigma-related conditions are synergistically driven by shared multilevel vulnerabilities. To address logistical and cost challenges while minimizing technology barriers and research distrust, we aim to establish a novel, hybrid community hub-supported digital cohort (N=3000). The digital cohort is the backbone of the study and is enhanced by hubs strategically located across the United States for increased engagement and in-person support. Study participants are English or Spanish speakers, are aged ≥18 years, identify as transgender women or along the transfeminine spectrum, reside in 1 of the 50 states or Puerto Rico, and do not have HIV (laboratory confirmed). Participants are followed for 24 months, with semiannual assessments. These include a questionnaire and laboratory-based HIV testing using self-collected specimens. Using residential zip codes, person-level data will be merged with contextual geolocated data, including population health measures and economic, housing, and other social and structural factors. Analyses will (1) evaluate the contribution of hub support to the digital cohort using descriptive statistics; (2) estimate and characterize syndemic patterns among transgender women using latent class analysis; (3) examine the role of contextual factors in driving syndemics and HIV prevention over time using multilevel regression models; (4) estimate HIV incidence in transgender women and examine the effect of syndemics and contextual factors on HIV incidence using Poisson regression models; and (5) develop dynamic, compartmental models of multilevel combination HIV prevention interventions among transgender women to simulate their impact on HIV incidence through 2030. Enrollment launched on March 15, 2023, with data collection phases occurring in spring and fall. As of February 24, 2024, a total of 3084 individuals were screened, and 996 (32.3%) met the inclusion criteria and enrolled into the cohort: 2.3% (23/996) enrolled at a hub, and 53.6% (534/996) enrolled through a community hub-supported strategy. Recruitment through purely digital methods contributed 61.5% (1895/3084) of those screened and 42.7% (425/996) of those enrolled in the cohort. Study findings will inform the development of evidence-based interventions to reduce HIV acquisition and syndemic conditions among US transgender women and advance efforts to end the US HIV epidemic. Methodological findings will also have critical implications for the design of future innovative approaches to HIV research. DERR1-10.2196/59846.

Spatiotemporal epidemiology of substance-related accidental acute toxicity deaths in Canada from 2016 to 2017

ObjectivesIn Canada, substance-related accidental acute toxicity deaths (AATDs) continue to rise at the national and sub-national levels. However, it is unknown if, where, when, and to what degree AATDs cluster in space, time, and space–time across the country. The objectives of this study were to 1) assess for clusters of AATDs that occurred in Canada during 2016 and 2017 at the national and provincial/territorial (P/T) levels, and 2) examine the substance types detected in AATD cases within each cluster.MethodsTwo years of person-level data on AATDs were abstracted from coroner and medical examiner files using a standardized data collection tool, including the decedent's postal code and municipality information on the places of residence, acute toxicity (AT) event, and death, and the substances detected in the death. Data were combined with Canadian census information to create choropleth maps depicting AATD rates by census division. Spatial scan statistics were used to build Poisson models to identify clusters of high rates (p < 0.05) of AATDs at the national and P/T levels in space, time, and space–time over the study period. AATD cases within clusters were further examined for substance types most present in each cluster.ResultsEight clusters in five regions of Canada at the national level and 24 clusters in 15 regions at the P/T level were identified, highlighting where AATDs occurred at far higher rates than the rest of the country. The risk ratios of identified clusters ranged from 1.28 to 9.62. Substances detected in clusters varied by region and time, however, opioids, stimulants, and alcohol were typically the most commonly detected substances within clusters.ConclusionOur findings are the first in Canada to reveal the geographic disparities in AATDs at national and P/T levels using spatial scan statistics. Rates associated with substance types within each cluster highlight which substance types were most detected in the identified regions. Findings may be used to guide intervention/program planning and provide a picture of the 2016 and 2017 context that can be used for comparisons of the geographic distribution of AATDs and substances with different time periods.

Representativeness of race, ethnicity and social determinants of health in patients with advanced non-small cell lung cancer via linkage of real-world electronic health records, administrative claims, and consumer financial data.

e13559 Background: Social determinants of health (SDOH) are both drivers of health disparities and barriers to clinical research participation, particularly among historically underserved or marginalized populations. Using real-world data (RWD), diversity plans in cancer clinical trials frequently aim to reduce disparities and improve representation. Our aim was to describe the representativeness of patients with advanced non-small cell lung cancer (aNSCLC) with respect to race, ethnicity and person-level SDOH in a curated electronic health record (EHR) database. Methods: This was a retrospective cohort study of individuals diagnosed with aNSCLC between 2016–2022 in a US-based oncology EHR database (ConcertAI). Information on person-level SDOH was derived from linkage of curated EHR data to administrative claims and consumer financial data. These data were also compared to a sample of patients diagnosed with first primary NSCLC from the Surveillance, Epidemiology, and End Results (SEER) Program and American Community Survey (ACS), where possible. The Kaplan-Meier product limit estimator was used to compare time from advanced diagnosis to treatment initiation. Results: From an overall sample of 17,772 patients with aNSCLC, a total of 10,390 patients had 1 or more SDOH characteristics of interest documented. Compared with patients without linked SDOH data, a higher proportion of patients with linked person-level SDOH data were non-Hispanic (NH) White patients (79% vs 71%) and lower proportions were NH Black (10% vs 13%), NH Asian/Pacific Islander (2% vs 4%) and Hispanic patients (2% vs 4%). Patients with linked SDOH were also more likely to have aNSCLC progressed from an earlier stage and received biomarker testing for targetable alterations. Overall, 54% of patients were married, 38% completed a bachelor’s degree or higher, 14% worked in a blue-collar occupation, and 61% had an individual income of ≤$35,000. All 4 of these SDOH characteristics differed significantly by race/ethnicity. Differences in time-to-treatment initiation were statistically significant by marital status (median of 50 [married] vs 55 [not married] days; P=0.02) and individual income (median of 50 [&gt;$60,000] vs 55 [≤$30,000] days; P=0.04). Patients with aNSCLC in these data differed from patients in SEER and ACS estimates with respect to greater representation of patients who are NH White, have lower levels of education, and lower individual income. Conclusions: This analysis demonstrated the feasibility of measuring person-level SDOH using EHR from patients with aNSCLC treated at oncology clinics across the US. RWD with linked SDOH characteristics should be used cautiously to address the representativeness gap in evidence from oncology trials given their potential inherent selection biases.

The cyclical cascade of HIV care: Temporal care engagement trends within a population-wide cohort.

The traditional HIV treatment cascade aims to visualise the journey of each person living with HIV from diagnosis, through initiation on antiretroviral therapy (ART) to treatment success, represented by virological suppression. This representation has been a pivotal tool in highlighting and quantifying sequential gaps along the care continuum. There is longstanding recognition, however, that this may oversimplify the complexity of real-world engagement with HIV services in settings with mature high-burden HIV epidemics. A complementary "cyclical" cascade has been proposed to represent the processes of disengagement at different points on the care continuum, with multiple pathways to re-engagement, although the feasibility of implementing this at scale has been uncertain. This study aimed to populate, refine, and explore the utility of a cyclical representation of the HIV cascade, using routine data from a high-burden HIV setting. This observational cohort study leveraged person-level data on all people living with HIV in the Western Cape (WC), South Africa, who accessed public health services in the 2 years prior to 31 December 2023. Programme data from disease registers were complemented by data from pharmacy and laboratory systems. At study closure, 494 370 people were included, constituting 93% of those of those estimated to be living with HIV in the province, of whom 355 104 were on ART. Substantial disengagement from HIV care was evident at every point on the cascade. Early treatment emerged as a period of higher risk of disengagement, but it did not account for the majority of disengagement. Almost all those currently disengaged had prior experience of treatment. While re-engagement was also common, overall treatment coverage had increased slowly over 5 years. The transition to dolutegravir-based regimens was dramatic with good virological outcomes for those in care, notwithstanding a clearly discernible impact of the Coronavirus Disease 2019 (COVID-19) pandemic on viral load (VL) testing. People currently engaged and disengaged in care are similar with respect to age and gender. Those who died or disengaged recently were previously distributed across a range of cascade statuses, and a substantial proportion of those newly initiating and re-initiating treatment were no longer on treatment 6 months later. The main limitation of this study was incomplete evidence of HIV testing, linkage to HIV-specific services, and out-of-facility mortality. Using routine data, it was possible to populate and automate a cyclical cascade of HIV care that continuously captured the nonlinear care journeys of individuals living with HIV. In this generalised mature HIV epidemic, most people are treatment experienced. Disengagement is common and occurs at various points along the cascade, making it challenging to identify high-impact intervention opportunities. While historical HIV cascades remain valuable for target setting and service monitoring, they can be complemented with insights from more detailed cyclical cascades.

The Association Between Personal Air Pollution Exposures and Fractional Exhaled Nitric Oxide (FeNO): A Systematic Review.

Airway inflammation is a common biological response to many types of environmental exposures and can lead to increased nitric oxide (NO) concentrations in exhaled breath. In recent years, several studies have evaluated airway inflammation using fractional exhaled nitric oxide (FeNO) as a biomarker of exposures to a range of air pollutants. This systematic review aims to summarize the studies that collected personal-level air pollution data to assess the air pollution-induced FeNO responses and to determine if utilizing personal-level data resulted in an improved characterization of the relationship between air pollution exposures and FeNO compared to using only ambient air pollution exposure data. Thirty-six eligible studies were identified. Overall, the studies included in this review establish that an increase in personal exposure to particulate and gaseous air pollutants can significantly increase FeNO. Nine out of the 12 studies reported statistically significant FeNO increases with increasing personal PM2.5 exposures, and up to 11.5% increase in FeNO per IQR increase in exposure has also been reported between FeNO and exposure to gas-phase pollutants, such as ozone, NO2, and benzene. Furthermore, factors such as chronic respiratory diseases, allergies, and medication use were found to be effect modifiers for air pollution-induced FeNO responses. About half of the studies that compared the effect estimates using both personal and ambient air pollution exposure methods reported that only personal exposure yielded significant associations with FeNO response. The evidence from the reviewed studies confirms that FeNO is a sensitive biomarker for air pollutant-induced airway inflammation. Personal air pollution exposure assessment is recommended to accurately assess the air pollution-induced FeNO responses. Furthermore, comprehensive adjustments for the potential confounding factors including the personal exposures of the co-pollutants, respiratory disease status, allergy status, and usage of medications for asthma and allergies are recommended while assessing the air pollution-induced FeNO responses.

Racial disparities in the selection of chief resident: A cross-sectional analysis of a national sample of senior residents in the United States

IntroductionPart of the difficulty in recruiting and retaining a diverse physician workforce, as well as within medical leadership, is due to racial disparities in medical education. We investigated whether self-identified race-ethnicity is associated with the likelihood of selection as chief resident (CR). Materials and methodsWe performed a cross sectional analysis using de-identified person-level data from the GME Track, a national resident database and tracking system, from 2015 through 2018. The exposure variable, self-identified race-ethnicity, was categorized as African American or Black, American Indian or Alaskan Native, Asian, Hispanic, Latino or of Spanish Origin, Native Hawaiian or Pacific Islander, White, and Multi-racial. The primary study outcome was CR selection among respondents in their final program year. Logistic regression was used to estimate the adjusted odds ratios (aOR) and 95% confidence intervals (CI) of CR selection for each racial group, as compared to the White referent group. ResultsAmong the study population (N=121,247), Black, Asian and Hispanic race-ethnicity was associated with a significantly decreased odds of being selected as CR in unadjusted and adjusted analyses. Black, Asian and Hispanic residents had a 26% (aOR=0.74, 95% CI 0.66-0.83), 29% (aOR=0.71, 95% CI 0.66-0.76) and 28% (aOR=0.72, 95% CI 0.66-0.94) decreased likelihood of becoming CR, respectively. Multi-racial residents also had a decreased likelihood, but to a lesser degree (aOR=0.92, 95% CI 0.89-0.95). ConclusionsIn as much as CR is an honor that sets one up for future opportunity, our findings suggest that residents of color are disproportionately disadvantaged compared to their White peers.

PP147 What Does Real World Evidence (RWE) Offer Health Technology Assessment (HTA) Procedures In Australia?

IntroductionMedical device health technology assessment (HTA) in Australia is largely coordinated by the Medical Services Advisory Committee (MSAC). Its remit to improve the public’s health by deciding where to allocate public healthcare funding, can be enhanced by considering real world evidence (RWE). Existing data sources have limitations that can be addressed through RWE, including coverage of Australian patient populations who may not meet trial eligibility criteria, and long-term follow-up through data linkage and datasets. We partnered with a university to explore what information could be gained from an analysis of linked administrative patient data, with a view to addressing current evidence gaps and/or limitations. The findings can be used as a source of local data to define patient populations, estimate actual costs of care, and enable more comprehensive economic modeling to inform medical device HTA.MethodsThe University-developed New South Wales Cardiovascular Cohort dataset, comprising person-level longitudinal NSW administrative data for all patients admitted to hospital with a cardiovascular diagnosis from 2001 onwards, linked to national Medicare Benefits Schedule and Pharmaceutical Benefits Scheme claims data, was interrogated.ResultsWorking with RWE is resource intensive in terms of time and costs. The potential of these data was revealed as the research progressed. It was possible to continually refine the data analyzed and reported,as well as expand the data requested. Varied expertise is required to accurately analyze the administrative datasets, particularly clinical classification skills and expertise in methods for causal inference using observational data. Findings from this study will enable the refinement of information for MSAC submissions, including identifying the most relevant patient population and reporting comprehensive costs, beyond an admitted hospital setting. The data will enhance engagement with clinicians and refine messaging, for example regarding patient risk factors.ConclusionsRWE enhances Australian HTA applications. Local data, extended periods of time and insights not apparent from a focus on admitted hospital episodes can be revealed. Data can be refined during the process for specificity and applicability.

Determining the Impact of Opioid Policy on Substance Use and Mental Health-Related Harms: Protocol for a Data Linkage Study.

Increasing harms related to prescription opioids over the past decade have led to the introduction of a range of key national and state policy initiatives across Australia. These include introducing a mandatory real-time prescription drug-monitoring program in the state of Victoria from April 2020 and a series of changes to subsidies for opioids on the Pharmaceutical Benefit Scheme from June 2020. Together, these changes aim to influence opioid supply and reduce harms related to prescription opioids, yet few studies have specifically explored how these policies have influenced opioid prescribing and related harms in Australia. The aim of this study is to examine the impact of a range of opioid-related policies on hospital admissions and emergency department (ED) presentations in Victoria, Australia. In particular, the study aims to understand the effect of various opioid policies and opioid-prescribing changes on (1) the number and rates of ED presentations and hospital admissions attributed to substance use (ie, opioid and nonopioid related) or mental ill-health (eg, suicide, self-harm, anxiety, and depression), (2) the association between differing opioid dose trajectories and the likelihood of ED presentations and hospital admissions related to substance use and mental ill-health, and (3) whether changes in an individual's opioid prescribing change the risk related to ED presentations and hospital admissions related to substance use and mental ill-health. We will conduct a population-level linked data study. General practice health records obtained from the Population Level Analysis and Reporting platform are linked with person-level data from 3 large hospital networks in Victoria, Australia. Interrupted time series analysis will be used to examine the impact of opioid policies on a range of harms, including the rates of presentations related to substance use (opioid and nonopioid) and mental ill-health among the primary care cohort. Group-based trajectory modeling and a case-crossover design will be used to further explore the impact of changes in opioid dosage and other covariates on opioid and nonopioid poisonings and mental ill-health-related presentations at the patient level. Given that this paper serves as a protocol, there are currently no results available. The deidentified primary health data were sourced from electronic medical records of approximately 4,717,000 patients from 542 consenting general practices over a 6-year period (2017-2022). The submission of results for publication is planned for early 2024. This study will add to the limited evidence base to help understand the impact of opioid policies in Australia, including whether intended or unintended outcomes are occurring as a result. EU PAS Register EUPAS104005; https://www.encepp.eu/encepp/viewResource.htm?id=104006. DERR1-10.2196/51825.

Essential requirements for the governance and management of data trusts, data repositories, and other data collaborations.

Around the world, many organisations are working on ways to increase the use, sharing, and reuse of person-level data for research, evaluation, planning, and innovation while ensuring that data are secure and privacy is protected. As a contribution to broader efforts to improve data governance and management, in 2020 members of our team published 12 minimum specification essential requirements (min specs) to provide practical guidance for organisations establishing or operating data trusts and other forms of data infrastructure. We convened an international team, consisting mostly of participants from Canada and the United States of America, to test and refine the original 12 min specs. Twenty-three (23) data-focused organisations and initiatives recorded the various ways they address the min specs. Sub-teams analysed the results, used the findings to make improvements to the min specs, and identified materials to support organisations/initiatives in addressing the min specs. Analyses and discussion led to an updated set of 15 min specs covering five categories: one min spec for Legal, five for Governance, four for Management, two for Data Users, and three for Stakeholder & Public Engagement. Multiple changes were made to make the min specs language more technically complete and precise. The updated set of 15 min specs has been integrated into a Canadian national standard that, to our knowledge, is the first to include requirements for public engagement and Indigenous Data Sovereignty. The testing and refinement of the min specs led to significant additions and improvements. The min specs helped the 23 organisations/initiatives involved in this project communicate and compare how they achieve responsible and trustworthy data governance and management. By extension, the min specs, and the Canadian national standard based on them, are likely to be useful for other data-focused organisations and initiatives.

How is city living associated with psychosis? Findings from a novel data linkage of 612,988 people from an urban and ethnically diverse area

ObjectivesUK cities show higher incidence of psychotic disorders, but the reasons remain unclear. This case-control study uses data from one of the first and largest person-level data linkages between mental health records and the UK census to explore associations previously only assessed using ecological or smaller studies in England.&#x0D; MethodsThe SocioEconomic Predictors of Mental Disorders (SEP-MD) project dataset comprises of data extracted from electronic health records (EHR) from the South London and Maudsley NHS Foundation trust (SLaM). These EHRs were linked to the 2011 UK census as a collaboration between SLaM, the ONS and King’s College London.&#x0D; Cases with clinical diagnoses of non-affective (schizophrenia-spectrum) and affective psychoses (bipolar disorder, depression with psychosis) were identified. Population controls were sampled from the locality.&#x0D; Logistic regression models were used to calculate weighted adjusted (age and sex) odds ratios (waOR) to assess associations. Robust standard errors were used to account for clustering.&#x0D; Results16,863 linked cases with psychosis (affective n=5,694; non-affective n=11,169) were identified alongside 596,125 population controls.&#x0D; Cases with psychosis were more likely to live in areas with the highest population density (waOR 1.17 (1.05, 1.30)) when comparing the lowest quintile to the highest. Non-affective disorders showed the highest association with population density.&#x0D; Being born within the UK was associated with a higher risk of psychosis, and migrants living in the country for longer were at a significantly higher risk than those living in the UK for less time. Socioeconomic predictors, including education, occupation and tenure, were all associated with higher psychosis risk. Racialised minorities were at higher risk of specifically non-affective psychoses. Indicators of isolation (marital status and living alone) were highly associated with psychosis risk.&#x0D; ConclusionsOur findings regarding urbanicity, ethnicity, migration socioeconomic position and social circumstances both confirm and provide further depth to previously identified associations. Novel findings relating to migration and interactions with ethnicity will require further investigation.&#x0D; These insights will provide valuable information for future public health and policy development.

Predicting the Public Health Impact of Bivalent Vaccines and Nirmatrelvir-Ritonavir Against Coronavirus Disease 2019.

Uptake of coronavirus disease 2019 (COVID-19) bivalent vaccines and the oral medication nirmatrelvir-ritonavir (Paxlovid) has remained low across the United States. Assessing the public health impact of increasing uptake of these interventions in key risk groups can guide further public health resources and policy and determine what proportion of severe COVID-19 is avertable with these interventions. This modeling study used person-level data from the California Department of Public Health on COVID-19 cases, hospitalizations, deaths, and vaccine administration from 23 July 2022 to 23 January 2023. We used a quasi-Poisson regression model calibrated to recent historical data to predict future COVID-19 outcomes and modeled the impact of increasing uptake (up to 70% coverage) of bivalent COVID-19 vaccines and nirmatrelvir-ritonavir during acute illness in different risk groups. Risk groups were defined by age (≥50, ≥65, ≥75 years) and vaccination status (everyone, primary series only, previously vaccinated). We predicted the number of averted COVID-19 cases, hospitalizations, and deaths and number needed to treat (NNT). The model predicted that increased uptake of bivalent COVID-19 boosters and nirmatrelvir-ritonavir (up to 70% coverage) in all eligible persons could avert an estimated 15.7% (95% uncertainty interval [UI], 11.2%-20.7%; NNT: 17 310) and 23.5% (95% UI, 13.1%-30.0%; NNT: 67) of total COVID-19-related deaths, respectively. In the high-risk group of persons ≥65 years old alone, increased uptake of bivalent boosters and nirmatrelvir-ritonavir could avert an estimated 11.9% (95% UI, 8.4%-15.1%; NNT: 2757) and 22.8% (95% UI, 12.7%-29.2%; NNT: 50) of total COVID-19-related deaths, respectively. These findings suggest that prioritizing uptake of bivalent boosters and nirmatrelvir-ritonavir among older age groups (≥65 years) would be most effective (based on NNT) but would not address the entire burden of severe COVID-19.